all_bib_full_refs.bib

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{gan2020large,
  title={Large-scale adversarial training for vision-and-language representation learning},
  author={Gan, Zhe and Chen, Yen-Chun and Li, Linjie and Zhu, Chen and Cheng, Yu and Liu, Jingjing},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6616--6628},
  year={2020}
}

@article{chen2019uniter,
  title={{UNITER}: Learning universal image-text representations},
  author={Chen, Yen-Chun and Li, Linjie and Yu, Licheng and El Kholy, Ahmed and Ahmed, Faisal and Gan, Zhe and Cheng, Yu and Liu, Jingjing},
  year={2019}
}

@inproceedings{kiela2021hateful,
  title={The hateful memes challenge: competition report},
  author={Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Fitzpatrick, Casey A and Bull, Peter and Lipstein, Greg and Nelli, Tony and Zhu, Ron and others},
  booktitle={NeurIPS 2020 Competition and Demonstration Track},
  pages={344--360},
  year={2021},
  organization={PMLR}
}

@article{su2019vl,
  title={{VL-BERT}: Pre-training of generic visual-linguistic representations},
  author={Su, Weijie and Zhu, Xizhou and Cao, Yue and Li, Bin and Lu, Lewei and Wei, Furu and Dai, Jifeng},
  journal={arXiv preprint arXiv:1908.08530},
  year={2019}
}

@article{nockleby2000hate,
  title={Hate speech},
  author={Nockleby, John T},
  journal={Encyclopedia of the American constitution},
  volume={3},
  number={2},
  pages={1277--1279},
  year={2000},
  publisher={Macmillan Detroit}
}

@inproceedings{fersini2019detecting,
  title={Detecting sexist MEME on the Web: A study on textual and visual cues},
  author={Fersini, Elisabetta and Gasparini, Francesca and Corchs, Silvia},
  booktitle={2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)},
  pages={226--231},
  year={2019},
  organization={IEEE}
}

@article{nakov2021detecting,
  title={Detecting abusive language on online platforms: A critical analysis},
  author={Nakov, Preslav and Nayak, Vibha and Dent, Kyle and Bhatawdekar, Ameya and Sarwar, Sheikh Muhammad and Hardalov, Momchil and Dinkov, Yoan and Zlatkova, Dimitrina and Bouchard, Guillaume and Augenstein, Isabelle},
  journal={arXiv preprint arXiv:2103.00153},
  year={2021}
}

@inproceedings{wulczyn2017ex,
  title={Ex machina: Personal attacks seen at scale},
  author={Wulczyn, Ellery and Thain, Nithum and Dixon, Lucas},
  booktitle={Proceedings of the 26th international conference on world wide web},
  pages={1391--1399},
  year={2017}
}
@inproceedings{hammer2014detecting,
  title={Detecting threats of violence in online discussions using bigrams of important words},
  author={Hammer, Hugo Lewi},
  booktitle={2014 IEEE Joint Intelligence and Security Informatics Conference},
  pages={319--319},
  year={2014},
  organization={IEEE}
}

@INPROCEEDINGS{8877435,  author={Hammer, Hugo L. and Riegler, Michael A. and Øvrelid, Lilja and Velldal, Erik},  booktitle={2019 International Conference on Content-Based Multimedia Indexing (CBMI)},   title={THREAT: A Large Annotated Corpus for Detection of Violent Threats},   year={2019},  volume={},  number={},  pages={1-5},  doi={10.1109/CBMI.2019.8877435}}

@article{doi:10.1080/23335777.2021.1940303,
author = {Huiling Yao and Xing Hu},
title = {A survey of video violence detection},
journal = {Cyber-Physical Systems},
volume = {0},
number = {0},
pages = {1-24},
year  = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/23335777.2021.1940303},

URL = { 
        https://doi.org/10.1080/23335777.2021.1940303
    
},
eprint = { 
        https://doi.org/10.1080/23335777.2021.1940303
    
}

}

@article{yao2021survey,
  title={A survey of video violence detection},
  author={Yao, Huiling and Hu, Xing},
  journal={Cyber-Physical Systems},
  pages={1--24},
  year={2021},
  publisher={Taylor \& Francis}
}
@article{krug2002world,
  title={The world report on violence and health},
  author={Krug, Etienne G and Mercy, James A and Dahlberg, Linda L and Zwi, Anthony B},
  journal={The lancet},
  volume={360},
  number={9339},
  pages={1083--1088},
  year={2002},
  publisher={Elsevier}
}

@article{ramzan2019review,
  title={A review on state-of-the-art violence detection techniques},
  author={Ramzan, Muhammad and Abid, Adnan and Khan, Hikmat Ullah and Awan, Shahid Mahmood and Ismail, Amina and Ahmed, Muzamil and Ilyas, Mahwish and Mahmood, Ahsan},
  journal={IEEE Access},
  volume={7},
  pages={107560--107575},
  year={2019},
  publisher={IEEE}
}

@inproceedings{10.1145/3038912.3052555,
author = {Wang, Yilin and Tang, Jiliang and Li, Jundong and Li, Baoxin and Wan, Yali and Mellina, Clayton and O'Hare, Neil and Chang, Yi},
title = {Understanding and Discovering Deliberate Self-Harm Content in Social Media},
year = {2017},
isbn = {9781450349130},
publisher = {International World Wide Web Conferences Steering Committee},
address = {Republic and Canton of Geneva, CHE},
url = {https://doi.org/10.1145/3038912.3052555},
doi = {10.1145/3038912.3052555},
abstract = {Studies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous and increasing volume of social media data, on-line self-harm content is likely to be buried rapidly by other normal content. To enable voices of self-harm users to be heard, it is important to distinguish self-harm content from other types of content. In this paper, we aim to understand self-harm content and provide automatic approaches to its detection. We first perform a comprehensive analysis on self-harm social media using different input cues. Our analysis, the first of its kind in large scale, reveals a number of important findings. Then we propose frameworks that incorporate the findings to discover self-harm content under both supervised and unsupervised settings. Our experimental results on a large social media dataset from Flickr demonstrate the effectiveness of the proposed frameworks and the importance of our findings in discovering self-harm content.},
booktitle = {Proceedings of the 26th International Conference on World Wide Web},
pages = {93–102},
numpages = {10},
keywords = {mental health, social media mining, self-harm detection, user modeling},
location = {Perth, Australia},
series = {WWW '17}
}

@inproceedings{parapar2021overview,
  title={Overview of erisk 2021: Early risk prediction on the internet},
  author={Parapar, Javier and Mart{\'\i}n-Rodilla, Patricia and Losada, David E and Crestani, Fabio},
  booktitle={International Conference of the Cross-Language Evaluation Forum for European Languages},
  pages={324--344},
  year={2021},
  organization={Springer}
}
@inproceedings{losada2020overview,
  title={Overview of eRisk at CLEF 2020: Early Risk Prediction on the Internet (Extended Overview).},
  author={Losada, David E and Crestani, Fabio and Parapar, Javier},
  booktitle={CLEF (Working Notes)},
  year={2020}
}

@inproceedings{10.1145/3018661.3018706,
author = {Wang, Tao and Brede, Markus and Ianni, Antonella and Mentzakis, Emmanouil},
title = {Detecting and Characterizing Eating-Disorder Communities on Social Media},
year = {2017},
isbn = {9781450346757},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3018661.3018706},
doi = {10.1145/3018661.3018706},
abstract = {Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.},
booktitle = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining},
pages = {91–100},
numpages = {10},
keywords = {text mining, mental health, eating disorder, social media, homophily, network analysis},
location = {Cambridge, United Kingdom},
series = {WSDM '17}
}

@article{doi:10.1177/1461444819850106,
author = {Florian Arendt and Sebastian Scherr and Daniel Romer},
title ={Effects of exposure to self-harm on social media: Evidence from a two-wave panel study among young adults},
journal = {New Media \& Society},
volume = {21},
number = {11-12},
pages = {2422-2442},
year = {2019},
doi = {10.1177/1461444819850106},

URL = { 
        https://doi.org/10.1177/1461444819850106
    
},
eprint = { 
        https://doi.org/10.1177/1461444819850106
    
}
}

@article{seko2018self,
  title={The self—harmed, visualized, and reblogged: Remaking of self-injury narratives on Tumblr},
  author={Seko, Yukari and Lewis, Stephen P},
  journal={New media \& society},
  volume={20},
  number={1},
  pages={180--198},
  year={2018},
  publisher={SAGE Publications Sage UK: London, England}
}

@Inbook{Laffier2016,
author="Laffier, Jennifer L.",
editor="Walrave, Michel
and Ponnet, Koen
and Vanderhoven, Ellen
and Haers, Jacques
and Segaert, Barbara",
title="Social Relations: Exploring How Youth Use Social Media to Communicate Signs and Symptoms of Depression and Suicidal Ideation",
bookTitle="Youth 2.0: Social Media and Adolescence: Connecting, Sharing and Empowering",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="161--178",
abstract="Over the last decade it has become apparent that social media may act as a vehicle for youth to communicate signs and symptoms of mental health distress. Therefore, this qualitative case study explored (1) how signs and symptoms of suicide ideation and severe depression can present within the context of social media and (2) how on-line signs and symptoms can be used to create screening tools for the general public. Content analysis of media reports, newspapers, and social media sites revealed several themes such as (1) loneliness, depression and hopelessness were key symptoms, (2) personality characteristics and frequency of social media use acted as mediators to identifying escalation of mental health distress, and (3) engaging in social media may provide both support and further pain for a person experiencing depression. These insights are used to make recommendations for future studies and the development of online screening tools.",
isbn="978-3-319-27893-3",
doi="10.1007/978-3-319-27893-3_9",
url="https://doi.org/10.1007/978-3-319-27893-3_9"
}


@inproceedings{singh2017toward,
  title={Toward multimodal cyberbullying detection},
  author={Singh, Vivek K and Ghosh, Souvick and Jose, Christin},
  booktitle={Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
  pages={2090--2099},
  year={2017}
}

@article{rosa2019automatic,
  title={Automatic cyberbullying detection: A systematic review},
  author={Rosa, Hugo and Pereira, N{\'a}dia and Ribeiro, Ricardo and Ferreira, Paula Costa and Carvalho, Joao Paulo and Oliveira, Sofia and Coheur, Lu{\'\i}sa and Paulino, Paula and Sim{\~a}o, AM Veiga and Trancoso, Isabel},
  journal={Computers in Human Behavior},
  volume={93},
  pages={333--345},
  year={2019},
  publisher={Elsevier}
}

@article{vogels2021state,
  title={The state of online harassment},
  author={Vogels, Emily A},
  journal={Pew Research Center},
  volume={13},
  year={2021}
}

@article{10.1371/journal.pone.0243300,
    doi = {10.1371/journal.pone.0243300},
    author = {Vidgen, Bertie AND Derczynski, Leon},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Directions in abusive language training data, a systematic review: Garbage in, garbage out},
    year = {2021},
    month = {12},
    volume = {15},
    url = {https://doi.org/10.1371/journal.pone.0243300},
    pages = {1-32},
    abstract = {Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.},
    number = {12},
}

@article{husain2021survey,
  title={A Survey of Offensive Language Detection for the Arabic Language},
  author={Husain, Fatemah and Uzuner, Ozlem},
  journal={ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)},
  volume={20},
  number={1},
  pages={1--44},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@INPROCEEDINGS{7920246,
  author={Haidar, Batoul and Chamoun, Maroun and Yamout, Fadi},
  booktitle={2016 European Modelling Symposium (EMS)}, 
  title={Cyberbullying Detection: A Survey on Multilingual Techniques}, 
  year={2016},
  volume={},
  number={},
  pages={165-171},
  doi={10.1109/EMS.2016.037}
  }



@inproceedings{afridi2021multimodal,
  title={A Multimodal Memes Classification: A Survey and Open Research Issues},
  author={Afridi, Tariq Habib and Alam, Aftab and Khan, Muhammad Numan and Khan, Jawad and Lee, Young Koo},
  booktitle={5th International Conference on Smart City Applications, SCA 2020},
  pages={1451--1466},
  year={2021},
  organization={Springer Science and Business Media Deutschland GmbH}
}

@article{alam2021survey,
  title={A Survey on Multimodal Disinformation Detection},
  author={Alam, Firoj and Cresci, Stefano and Chakraborty, Tanmoy and Silvestri, Fabrizio and Dimitrov, Dimiter and Martino, Giovanni Da San and Shaar, Shaden and Firooz, Hamed and Nakov, Preslav},
  journal={arXiv preprint arXiv:2103.12541},
  year={2021}
}

@article{hegde2021images,
  title={Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification},
  author={Hegde, Siddhanth U and Hande, Adeep and Priyadharshini, Ruba and Thavareesan, Sajeetha and Sakuntharaj, Ratnasingam and Thangasamy, Sathiyaraj and Bharathi, B and Chakravarthi, Bharathi Raja},
  journal={arXiv preprint arXiv:2108.03886},
  year={2021}
}

@inproceedings{suryawanshi-etal-2020-dataset,
    title = "A Dataset for Troll Classification of {T}amil{M}emes",
    author = "Suryawanshi, Shardul  and
      Chakravarthi, Bharathi Raja  and
      Verma, Pranav  and
      Arcan, Mihael  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2020.wildre-1.2",
    pages = "7--13",
    abstract = "Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}


@book{shifman2013memes,
  title={Memes in digital culture},
  author={Shifman, Limor},
  year={2013},
  publisher={MIT press}
}

@inproceedings{mubarak2017abusive,
  title={Abusive language detection on Arabic social media},
  author={Mubarak, Hamdy and Darwish, Kareem and Magdy, Walid},
  booktitle={Proceedings of the first workshop on abusive language online},
  pages={52--56},
  year={2017}
}

@inproceedings{kumar2018benchmarking,
  title={Benchmarking aggression identification in social media},
  author={Kumar, Ritesh and Ojha, Atul Kr and Malmasi, Shervin and Zampieri, Marcos},
  booktitle={Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)},
  pages={1--11},
  year={2018}
}

@article{fortuna2018survey,
  title={A survey on automatic detection of hate speech in text},
  author={Fortuna, Paula and Nunes, S{\'e}rgio},
  journal={ACM Computing Surveys (CSUR)},
  volume={51},
  number={4},
  pages={1--30},
  year={2018},
  publisher={ACM New York, NY, USA}
}

@inproceedings{suryawanshi2020multimodal,
  title={Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text},
  author={Suryawanshi, Shardul and Chakravarthi, Bharathi Raja and Arcan, Mihael and Buitelaar, Paul},
  booktitle={Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying},
  pages={32--41},
  year={2020}
}

@article{sabat2019hate,
  title={Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation},
  author={Sabat, Benet Oriol and Ferrer, Cristian Canton and Giro-i-Nieto, Xavier},
  year={2019}
}

@INPROCEEDINGS{9455994,
  author={Zhou, Yi and Chen, Zhenhao and Yang, Huiyuan},
  booktitle={2021 IEEE International Conference on Multimedia   Expo Workshops (ICMEW)}, 
  title={Multimodal Learning For Hateful Memes Detection}, 
  year={2021},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ICMEW53276.2021.9455994}}


@inproceedings{pramanick2021detecting,
  title={Detecting Harmful Memes and Their Targets},
  author={Pramanick, Shraman and Dimitrov, Dimitar and Mukherjee, Rituparna and Sharma, Shivam and Akhtar, Md Shad and Nakov, Preslav and Chakraborty, Tanmoy},
  booktitle={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  pages={2783--2796},
  year={2021}
}

@inproceedings{pramanick-etal-2021-momenta-multimodal,
    title = "{MOMENTA}: A Multimodal Framework for Detecting Harmful Memes and Their Targets",
    author = "Pramanick, Shraman  and
      Sharma, Shivam  and
      Dimitrov, Dimitar  and
      Akhtar, Md. Shad  and
      Nakov, Preslav  and
      Chakraborty, Tanmoy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.379",
    doi = "10.18653/v1/2021.findings-emnlp.379",
    pages = "4439--4455",
    abstract = "Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. In particular, we focus on two tasks: (i)detecting harmful memes, and (ii) identifying the social entities they target. We further extend the recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.",
}


@inproceedings{mroueh2015deep,
  title={Deep multimodal learning for audio-visual speech recognition},
  author={Mroueh, Youssef and Marcheret, Etienne and Goel, Vaibhava},
  booktitle={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={2130--2134},
  year={2015},
  organization={IEEE}
}

@article{liu2021cptr,
  title={Cptr: Full transformer network for image captioning},
  author={Liu, Wei and Chen, Sihan and Guo, Longteng and Zhu, Xinxin and Liu, Jing},
  journal={arXiv preprint arXiv:2101.10804},
  year={2021}
}
@article{zhu2021deep,
  title={Deep audio-visual learning: A survey},
  author={Zhu, Hao and Luo, Man-Di and Wang, Rui and Zheng, Ai-Hua and He, Ran},
  journal={International Journal of Automation and Computing},
  pages={1--26},
  year={2021},
  publisher={Springer}
}



@inproceedings{10.1088/1742-6596/1237/2/022144,
  title={A review of audio-visual fusion with machine learning},
  author={Song, Xiaoyu and Chen, Hong and Wang, Qing and Chen, Yunqiang and Tian, Mengxiao and Tang, Hui},
  booktitle={Journal of Physics: Conference Series},
  volume={1237},
  number={2},
  pages={022144},
  year={2019},
  organization={IOP Publishing}
}

@article{chen2021heu,
  title={HEU Emotion: a large-scale database for multimodal emotion recognition in the wild},
  author={Chen, Jing and Wang, Chenhui and Wang, Kejun and Yin, Chaoqun and Zhao, Cong and Xu, Tao and Zhang, Xinyi and Huang, Ziqiang and Liu, Meichen and Yang, Tao},
  journal={Neural Computing and Applications},
  pages={1--17},
  year={2021},
  publisher={Springer}
}

@article{summaira2021recent,
      title={Recent Advances and Trends in Multimodal Deep Learning: A Review}, 
      author={Jabeen Summaira and Xi Li and Amin Muhammad Shoib and Songyuan Li and Jabbar Abdul},
      year={2021},
      journal={arXiv: 2105.11087},
      eprint={2105.11087},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{ngiam2011multimodal,
  title={Multimodal deep learning},
  author={Ngiam, Jiquan and Khosla, Aditya and Kim, Mingyu and Nam, Juhan and Lee, Honglak and Ng, Andrew Y},
  booktitle={ICML},
  year={2011}
}
@inproceedings{kotonya-toni-2020-explainable,
    title = "Explainable Automated Fact-Checking: A Survey",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.474",
    doi = "10.18653/v1/2020.coling-main.474",
    pages = "5430--5443",
    abstract = "A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality {--} that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.",
}

@inproceedings{dimitrov2021detecting,
    title = "Detecting Propaganda Techniques in Memes",
    author = "Dimitrov, Dimitar  and
      Bin Ali, Bishr  and
      Shaar, Shaden  and
      Alam, Firoj  and
      Silvestri, Fabrizio  and
      Firooz, Hamed  and
      Nakov, Preslav  and
      Da San Martino, Giovanni",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.516",
    doi = "10.18653/v1/2021.acl-long.516",
    pages = "6603--6617",
    abstract = "Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. However, it is with the advent of the Internet and the social media that propaganda has started to spread on a much larger scale than before, thus becoming major societal and political issue. Nowadays, a large fraction of propaganda in social media is multimodal, mixing textual with visual content. With this in mind, here we propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes. We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both. Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques. This is further confirmed in our experiments with several state-of-the-art multimodal models.",
  series    = {ACL-IJCNLP~'21},
}

@inproceedings{waseem-hovy-2016-hateful,
    title = "Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on {T}witter",
    author = "Waseem, Zeerak  and
      Hovy, Dirk",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N16-2013",
    doi = "10.18653/v1/N16-2013",
    pages = "88--93",
}

@inproceedings{qian-etal-2018-hierarchical,
    title = "Hierarchical {CVAE} for Fine-Grained Hate Speech Classification",
    author = "Qian, Jing  and
      ElSherief, Mai  and
      Belding, Elizabeth  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1391",
    doi = "10.18653/v1/D18-1391",
    pages = "3550--3559",
    abstract = "Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories. In this paper, we propose a novel method on a fine-grained hate speech classification task, which focuses on differentiating among 40 hate groups of 13 different hate group categories. We first explore the Conditional Variational Autoencoder (CVAE) as a discriminative model and then extend it to a hierarchical architecture to utilize the additional hate category information for more accurate prediction. Experimentally, we show that incorporating the hate category information for training can significantly improve the classification performance and our proposed model outperforms commonly-used discriminative models.",
}

@article{cooper2007concise,
  title={A Concise History of the Fauxtography Blogstorm in the 2006 Lebanon War},
  author={Cooper, Stephen D},
  journal={American Communication Journal},
  volume={9},
  number={2},
  year={2007}
}

@inproceedings{kazemi-etal-2021-claim,
    title = "Claim Matching Beyond {E}nglish to Scale Global Fact-Checking",
    author = "Kazemi, Ashkan  and
      Garimella, Kiran  and
      Gaffney, Devin  and
      Hale, Scott",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    series = "ACL-IJCNLP~'21",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.347",
    doi = "10.18653/v1/2021.acl-long.347",
    pages = "4504--4517",
}

@inproceedings{wan-etal-2021-dqn,
    title = "A {DQN}-based Approach to Finding Precise Evidences for Fact Verification",
    author = "Wan, Hai  and
      Chen, Haicheng  and
      Du, Jianfeng  and
      Luo, Weilin  and
      Ye, Rongzhen",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    series = "ACL-IJCNLP~'21",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.83",
    doi = "10.18653/v1/2021.acl-long.83",
    pages = "1030--1039",
}

@inproceedings{jiang-etal-2021-exploring-listwise,
    title = "Exploring Listwise Evidence Reasoning with T5 for Fact Verification",
    author = "Jiang, Kelvin  and
      Pradeep, Ronak  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    series = "ACL-IJCNLP~'21",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-short.51",
    doi = "10.18653/v1/2021.acl-short.51",
    pages = "402--410",
}
@inproceedings{si-etal-2021-topic,
    title = "Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification",
    author = "Si, Jiasheng  and
      Zhou, Deyu  and
      Li, Tongzhe  and
      Shi, Xingyu  and
      He, Yulan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.128",
    doi = "10.18653/v1/2021.acl-long.128",
    pages = "1612--1622",
}


@article{shaar2021assisting,
      title={Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document}, 
      author={Shaden Shaar and Firoj Alam and Giovanni Da San Martino and Preslav Nakov},
      year={2021},
      journal={arXiv preprint arXiv:2109.07410},
      eprint={2109.07410},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@Article{claim:retrieval:context:2021,
  author	= {Shaden Shaar and Firoj Alam and Da San Martino, Giovanni
		  and Preslav Nakov},
  title		= {The Role of Context in Detecting Previously Fact-Checked
		  Claims},
  journal	= {Arxiv/2104.07423},
  year		= {2021}
}


@inproceedings{ma2016detecting,
  title={Detecting rumors from microblogs with recurrent neural networks},
  author={Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J and Wong, Kam Fai and Cha, Meeyoung},
  booktitle={IJCAI International Joint Conference on Artificial Intelligence},
  volume={2016},
  pages={3818--3824},
  year={2016}
}

@inproceedings{ma2017detect,
    title = "Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning",
    author = "Ma, Jing  and
      Gao, Wei  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1066",
    doi = "10.18653/v1/P17-1066",
    pages = "708--717",
}


@InProceedings{10.1007/978-3-030-73696-5_3,
author="Patwa, Parth
and Sharma, Shivam
and Pykl, Srinivas
and Guptha, Vineeth
and Kumari, Gitanjali
and Akhtar, Md Shad
and Ekbal, Asif
and Das, Amitava
and Chakraborty, Tanmoy",
editor="Chakraborty, Tanmoy
and Shu, Kai
and Bernard, H. Russell
and Liu, Huan
and Akhtar, Md Shad",
title="Fighting an Infodemic: COVID-19 Fake News Dataset",
booktitle="Combating Online Hostile Posts in Regional Languages during Emergency Situation",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="21--29",
abstract="Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We perform a binary classification task (real vs fake) and benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32{\%} F1-score with SVM on the test set. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection.",
isbn="978-3-030-73696-5"
}



@article{zarocostas2020fight,
  title={How to fight an infodemic},
  author={Zarocostas, John},
  journal={The lancet},
  volume={395},
  number={10225},
  pages={676},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{sathe-etal-2020-automated,
    title = "Automated Fact-Checking of Claims from {W}ikipedia",
    author = "Sathe, Aalok  and
      Ather, Salar  and
      Le, Tuan Manh  and
      Perry, Nathan  and
      Park, Joonsuk",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    series = "ACL~'20",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.849",
    pages = "6874--6882",
    abstract = "Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. To this end, we present WikiFactCheck-English, a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations, as well as 34k+ manually written claims that are refuted by the evidence documents. This is the largest fact checking dataset consisting of real claims and evidence to date; it will allow the development of fact checking systems that can better process claims and evidence in the real world. We also show that for the NLI subtask, a logistic regression system trained using existing and novel features achieves peak accuracy of 68{\%}, providing a competitive baseline for future work. Also, a decomposable attention model trained on SNLI significantly underperforms the models trained on this dataset, suggesting that models trained on manually generated data may not be sufficiently generalizable or suitable for fact checking real-world claims.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

@inproceedings{atanasova-etal-2020-generating-fact,
    title = "Generating Fact Checking Explanations",
    author = "Atanasova, Pepa  and
      Simonsen, Jakob Grue  and
      Lioma, Christina  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    series = "ACL~'20",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.656",
    doi = "10.18653/v1/2020.acl-main.656",
    pages = "7352--7364",
    abstract = "Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process {--} generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.",
}



@inproceedings{alam2020fighting,
    title={Fighting the {COVID}-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society},
    author={Firoj Alam and Shaden Shaar and Fahim Dalvi and Hassan Sajjad and Alex Nikolov and Hamdy Mubarak and Giovanni Da San Martino and Ahmed Abdelali and Nadir Durrani and Kareem Darwish and Abdulaziz Al-Homaid and Wajdi Zaghouani and Tommaso Caselli and Gijs Danoe and Friso Stolk and Britt Bruntink and Preslav Nakov},
    booktitle = {Findings of EMNLP 2021},
    year={2021},
    publisher	= "Association for Computational Linguistics",
    series	= {EMNLP~'21},    
    
}

 

@InProceedings{alam2020call2arms,
  title		= {Fighting the {COVID}-19 Infodemic in Social Media: A
		  Holistic Perspective and a Call to Arms},
  author	= {Alam, Firoj and Dalvi, Fahim and Shaar, Shaden and
		  Durrani, Nadir and Mubarak, Hamdy and Nikolov, Alex and {Da
		  San Martino}, Giovanni and Abdelali, Ahmed and Sajjad,
		  Hassan and Darwish, Kareem and Nakov, Preslav},
  year		= {2021},
  pages		= {913-922},
  nomonth	= {May},
  NOvolume	= {15},
  booktitle	= {Proceedings of the International {AAAI} Conference on Web
		  and Social Media},
  series	= {ICWSM~'21},
  nourl		= {https://ojs.aaai.org/index.php/ICWSM/article/view/18114}
}

@book{ireton2018journalism,
  title={Journalism, fake news \& disinformation: handbook for journalism education and training},
  author={Ireton, Cherilyn and Posetti, Julie},
  year={2018},
  publisher={Unesco Publishing}
}

@book{cooke2018fake,
  title={Fake news and alternative facts: Information literacy in a post-truth era},
  author={Cooke, Nicole A},
  year={2018},
  publisher={American Library Association}
}

@inproceedings{nakov-da-san-martino-2020-fact,
    title = "Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era",
    author = "Nakov, Preslav  and
      Da San Martino, Giovanni",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-tutorials.2",
    doi = "10.18653/v1/2020.emnlp-tutorials.2",
    pages = "7--19",
}

@inproceedings{
Chen2020TabFact:,
title={TabFact: A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen and Hongmin Wang and Jianshu Chen and Yunkai Zhang and Hong Wang and Shiyang Li and Xiyou Zhou and William Yang Wang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rkeJRhNYDH}
}

@article{harkin2012deciphering,
  title={Deciphering user-generated content in transitional societies},
  author={Harkin, Juliette and Anderson, Kevin and Morgan, Libby and Smith, Briar},
  journal={Philadelphia, PA: Center for Global Communication Studies, Annenberg School for Communication, University of Pennsylvania},
  year={2012}
}

@article{doi:10.1177/2056305117717888,
author = {Adrian Rauchfleisch and Xenia Artho and Julia Metag and Senja Post and Mike S. Schäfer},
title ={How journalists verify user-generated content during terrorist crises. Analyzing Twitter communication during the Brussels attacks},
journal = {Social Media + Society},
volume = {3},
number = {3},
pages = {2056305117717888},
year = {2017},
doi = {10.1177/2056305117717888},
URL = {https://doi.org/10.1177/2056305117717888},
eprint = { 
        https://doi.org/10.1177/2056305117717888},
    abstract = { Social media, and Twitter in particular, have become important sources for journalists in times of crises. User-generated content (UGC) can provide journalists with on-site information and material they otherwise would not have access to. But how they source and verify UGC has not yet been systematically analyzed. This study analyzes sourcing and verification practices on Twitter during the Brussels attacks in March 2016. Based on quantitative content analysis, we identified (1) the journalists and news organizations sourcing during the attacks, (2) classified different forms of sourcing and verification requests, and (3) analyzed the sourced UGC. Results show that sourcing on Twitter has become a global phenomenon. During the first hours of the attack, journalists rely on UGC. Their sourcing and verification practices vary widely and often lack basic verification procedures, which leads to a discussion about the ethical implications of sourcing practices. }
}


@inproceedings{banko-etal-2020-unified,
    title = "A Unified Taxonomy of Harmful Content",
    author = "Banko, Michele  and
      MacKeen, Brendon  and
      Ray, Laurie",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.alw-1.16",
    doi = "10.18653/v1/2020.alw-1.16",
    pages = "125--137",
    abstract = "The ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse. While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation. As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.",
}

@article{LEWANDOWSKY2017353,
title = {Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era},
journal = {Journal of Applied Research in Memory and Cognition},
volume = {6},
number = {4},
pages = {353-369},
year = {2017},
issn = {2211-3681},
doi = {https://doi.org/10.1016/j.jarmac.2017.07.008},
url = {https://www.sciencedirect.com/science/article/pii/S2211368117300700},
author = {Stephan Lewandowsky and Ullrich K.H. Ecker and John Cook},
keywords = {Misinformation, Fake news, Post-truth politics, Demagoguery},
abstract = {The terms “post-truth” and “fake news” have become increasingly prevalent in public discourse over the last year. This article explores the growing abundance of misinformation, how it influences people, and how to counter it. We examine the ways in which misinformation can have an adverse impact on society. We summarize how people respond to corrections of misinformation, and what kinds of corrections are most effective. We argue that to be effective, scientific research into misinformation must be considered within a larger political, technological, and societal context. The post-truth world emerged as a result of societal mega-trends such as a decline in social capital, growing economic inequality, increased polarization, declining trust in science, and an increasingly fractionated media landscape. We suggest that responses to this malaise must involve technological solutions incorporating psychological principles, an interdisciplinary approach that we describe as “technocognition.” We outline a number of recommendations to counter misinformation in a post-truth world.}
}

@inproceedings{van-hee-etal-2015-detection,
    title = "Detection and Fine-Grained Classification of Cyberbullying Events",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Verhoeven, Ben  and
      Mennes, Julie  and
      Desmet, Bart  and
      De Pauw, Guy  and
      Daelemans, Walter  and
      Hoste, Veronique",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://aclanthology.org/R15-1086",
    pages = "672--680",
}

@inproceedings{schmidt-wiegand-2017-survey,
    title = "A Survey on Hate Speech Detection using Natural Language Processing",
    author = "Schmidt, Anna  and
      Wiegand, Michael",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1101",
    doi = "10.18653/v1/W17-1101",
    pages = "1--10",
    abstract = "This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.",
}

@inproceedings{joksimovic-etal-2019-automated,
    title = "Automated Identification of Verbally Abusive Behaviors in Online Discussions",
    author = "Joksimovic, Srecko  and
      Baker, Ryan S.  and
      Ocumpaugh, Jaclyn  and
      Andres, Juan Miguel L.  and
      Tot, Ivan  and
      Wang, Elle Yuan  and
      Dawson, Shane",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-3505",
    doi = "10.18653/v1/W19-3505",
    pages = "36--45",
    abstract = "Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.",
}

@inproceedings{brooke-2019-condescending,
    title = "{``}Condescending, Rude, Assholes{''}: Framing gender and hostility on {S}tack {O}verflow",
    author = "Brooke, Sian",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-3519",
    doi = "10.18653/v1/W19-3519",
    pages = "172--180",
    abstract = "The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.",
}

@InProceedings{d18-1389,
  author	= "Baly, Ramy and Karadzhov, Georgi and Alexandrov, Dimitar
		  and Glass, James and Nakov, Preslav",
  title		= "Predicting Factuality of Reporting and Bias of News Media
		  Sources",
  booktitle	= "Proceedings of the Conference on Empirical Methods in
		  Natural Language Processing",
  series	= {EMNLP~'18},
  year		= "2018",
  address	= "Brussels, Belgium",
  publisher	= "Association for Computational Linguistics",
  pages		= "3528--3539",
  addres	= "Brussels, Belgium",
  nonourl	= "http://aclweb.org/anthology/D18-1389"
}


@inproceedings{baly2020written,
    title = "What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context",
    author = "Baly, Ramy  and
      Karadzhov, Georgi  and
      An, Jisun  and
      Kwak, Haewoon  and
      Dinkov, Yoan  and
      Ali, Ahmed  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    series	= {ACL~'20},
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.308",
    doi = "10.18653/v1/2020.acl-main.308",
    pages = "3364--3374",
}

@article{sen2020multimodal,
  title={Multimodal Deception Detection using Real-Life Trial Data},
  author={Sen, Umut Mehmet and Perez-Rosas, Veronica and Yanikoglu, Berrin and Abouelenien, Mohamed and Burzo, Mihai and Mihalcea, Rada},
  journal={IEEE Transactions on Affective Computing},
  year={2020},
  publisher={IEEE}
}

@article{heller2018ps,
  title={The ps-battles dataset-an image collection for image manipulation detection},
  author={Heller, Silvan and Rossetto, Luca and Schuldt, Heiko},
  journal={arXiv preprint arXiv:1804.04866},
  year={2018}
}

@comment{@INPROCEEDINGS{Canini:2011,
author={Kevin R. Canini and Bongwon Suh and Peter L. Pirolli},
title={Finding Credible Information Sources in Social Networks Based on Content and Social Structure},
booktitle = {Proceedings of the IEEE International Conference on Privacy, Security, Risk, and Trust, and the IEEE International Conference on Social Computing},
series = {SocialCom/PASSAT~'11},
address = {Boston, MA, USA},
year={2011},
pages={1-8},
keywords={information management;social networking (online);user interfaces;content structure;credibility judgment;information credibility;information quality;information relevance;information source;social network user identification;social network user ranking;social structure;source credibility;Algorithm design and analysis;Analytical models;Appraisal;Tag clouds;Twitter;Visualization},
NOdoi={10.1109/PASSAT/SocialCom.2011.91},}

@comment{@INPROCEEDINGS{Canini:2011,
author={Kevin R. Canini and Bongwon Suh and Peter L. Pirolli},
title={Finding Credible Information Sources in Social Networks Based on Content and Social Structure},
booktitle = {Proceedings of the IEEE International Conference on Privacy, Security, Risk, and Trust, and the IEEE International Conference on Social Computing},
series = {SocialCom/PASSAT~'11},
address = {Boston, MA, USA},
year={2011},
pages={1-8},
keywords={information management;social networking (online);user interfaces;content structure;credibility judgment;information credibility;information quality;information relevance;information source;social network user identification;social network user ranking;social structure;source credibility;Algorithm design and analysis;Analytical models;Appraisal;Tag clouds;Twitter;Visualization},
NOdoi={10.1109/PASSAT/SocialCom.2011.91},}

@comment{@article{3d-cnn, 
author={S. {Ji} and W. {Xu} and M. {Yang} and K. {Yu}}, 
journal={PAMI}, 
title={{3D} Convolutional Neural Networks for Human Action Recognition}, 
year={2013}, 
volume={35}, 
number={1}, 
pages={221-231}, 
keywords={feature extraction;gesture recognition;image classification;image motion analysis;image representation;neural nets;spatiotemporal phenomena;video surveillance;3D convolutional neural networks;automated human action recognition;complex handcrafted features;deep model;3D CNN model;temporal dimensions;spatial dimensions;motion information encoding;feature representation;high-level features;airport surveillance videos;baseline methods;Three dimensional displays;Solid modeling;Feature extraction;Computer architecture;Videos;Kernel;Computational modeling;Deep learning;convolutional neural networks;3D convolution;model combination;action recognition;Algorithms;Decision Support Techniques;Image Interpretation, Computer-Assisted;Imaging, Three-Dimensional;Movement;Neural Networks (Computer);Pattern Recognition, Automated;Subtraction Technique}, 
NOdoi={10.1109/TPAMI.2012.59}, 
ISSN={0162-8828},}

@comment{@INPROCEEDINGS{Kwon:2013, 
author={Sejeong Kwon and Meeyoung Cha and Kyomin Jung and Wei Chen and Yajun Wang}, 
title={Prominent Features of Rumor Propagation in Online Social Media}, 
booktitle={Proceedings of the 13th IEEE International Conference on Data Mining}, 
series = {ICDM~'13},
year={2013}, 
pages={1103-1108}, 
address = {Dallas, TX, USA},
keywords={social networking (online);time series;daily shock cycles;external shock cycles;linguistic differences;online social media;online social networks;periodic time series model;rumor classification;rumor propagation;structural differences;temporal characteristics;Adaptation models;Electric shock;Mathematical model;Pragmatics;Psychology;Time series analysis;Twitter;Diffusion Network;Rumor;Sentiment Analysis;Social Media;Time Series}, 
NOdoi={10.1109/ICDM.2013.61}, 
ISSN={1550-4786},}

@comment{@INPROCEEDINGS{Kwon:2013, 
author={Sejeong Kwon and Meeyoung Cha and Kyomin Jung and Wei Chen and Yajun Wang}, 
title={Prominent Features of Rumor Propagation in Online Social Media}, 
booktitle={Proceedings of the 13th IEEE International Conference on Data Mining}, 
series = {ICDM~'13},
year={2013}, 
pages={1103-1108}, 
address = {Dallas, TX, USA},
NOdoi={10.1109/ICDM.2013.61}, 
ISSN={1550-4786},}

@inproceedings{10.1007/978-3-030-16447-8_10,
 address = {Cham},
 author = {Silva, Carlos
and Santos, Ricardo
and Barbosa, Ricardo},
 booktitle = {Intelligent Technologies for Interactive Entertainment},
 editor = {Cortez, Paulo
and Magalh{\~a}es, Lu{\'i}s
and Branco, Pedro
and Portela, Carlos Filipe
and Ad{\~a}o, Telmo},
 isbn = {978-3-030-16447-8},
 pages = {95--104},
 publisher = {Springer International Publishing},
 title = {Detection and Prevention of Bullying on Online Social Networks: The Combination of Textual, Visual and Cognitive},
 year = {2019}
}



@inproceedings{10.1145/2502081.2502187,
author = {Acar, Esra and Hopfgartner, Frank and Albayrak, Sahin},
title = {Violence Detection in Hollywood Movies by the Fusion of Visual and Mid-Level Audio Cues},
year = {2013},
isbn = {9781450324045},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2502081.2502187},
doi = {10.1145/2502081.2502187},
booktitle = {Proceedings of the 21st ACM International Conference on Multimedia},
pages = {717–720},
numpages = {4},
keywords = {decision fusion, bag-of-audio-words, support vector machine, motion, mel-frequency cepstral coefficients},
location = {Barcelona, Spain},
series = {MM '13}
}



@inproceedings{10.1145/3041021.3055135,
 address = {Republic and Canton of Geneva, CHE},
 author = {Cresci, Stefano and Di Pietro, Roberto and Petrocchi, Marinella and Spognardi, Angelo and Tesconi, Maurizio},
 booktitle = {WWW},
 doi = {10.1145/3041021.3055135},
 isbn = {9781450349147},
 keywords = {twitter, spam, social spambots, automation, social networks security},
 numpages = {10},
 pages = {963–972},
 publisher = {International World Wide Web Conferences Steering Committee},
 series = {WWW '17 Companion},
 title = {The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race},
 url = {https://doi.org/10.1145/3041021.3055135},
 year = {2017}
}

@inproceedings{10.1145/3340531.3412046,
 author = {Van{-}Hoang Nguyen and
Kazunari Sugiyama and
Preslav Nakov and
Min{-}Yen Kan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cikm/NguyenSNK20.bib},
 booktitle = {The 29th {ACM} International Conference on Information
and Knowledge Management},
 doi = {10.1145/3340531.3412046},
 editor = {Mathieu d'Aquin and
Stefan Dietze and
Claudia Hauff and
Edward Curry and
Philippe Cudr{\'{e}}{-}Mauroux},
 pages = {1165--1174},
 publisher = {{ACM}},
 timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
 title = {{FANG:} Leveraging Social Context for Fake News Detection Using Graph
Representation},
 url = {https://doi.org/10.1145/3340531.3412046},
 year = {2020}
}

@article{10.1145/3395046,
 author = {Zhou, Xinyi and Zafarani, Reza},
 eprint = {1812.00315},
 journal = {CSUR},
 noarchiveprefix = {arXiv},
 noarxivid = {1812.00315},
 nodoi = {10.1145/3395046},
 noissn = {15577341},
 number = {5},
 title = {A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities},
 volume = {53},
 year = {2020}
}

@article{10.1371journal.pone.0128193,
 abstract = {Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.},
 author = {Ciampaglia, Giovanni Luca AND Shiralkar, Prashant AND Rocha, Luis M. AND Bollen, Johan AND Menczer, Filippo AND Flammini, Alessandro},
 journal = {PLOS ONE},
 nodoi = {10.1371/journal.pone.0128193},
 nourl = {https://doi.org/10.1371/journal.pone.0128193},
 number = {6},
 pages = {1-13},
 publisher = {Public Library of Science},
 title = {Computational Fact Checking from Knowledge Networks},
 volume = {10},
 year = {2015}
}

@inproceedings{7193253,
 author = { {Arun Anoop M}},
 booktitle = {2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)},
 number = {},
 pages = {1-9},
 title = {Image forgery and its detection: A survey},
 volume = {},
 year = {2015}
}

@inproceedings{7193253,
 author = { {Arun Anoop M}},
 booktitle = {2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)},
 number = {},
 pages = {1-9},
 title = {Image forgery and its detection: A survey},
 volume = {},
 year = {2015}
}

@inproceedings{8970810,
 author = {S. {Dutta} and D. {Das} and T. {Chakraborty}},
 booktitle = {2019 IEEE International Conference on Data Mining (ICDM)},
 number = {},
 pages = {180-189},
 title = {Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory},
 volume = {},
 year = {2019}
}

@inproceedings{8970810,
 author = {S. {Dutta} and D. {Das} and T. {Chakraborty}},
 booktitle = {2019 IEEE International Conference on Data Mining (ICDM)},
 number = {},
 pages = {180-189},
 title = {Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory},
 volume = {},
 year = {2019}
}

@article{9064936,
 author = {M. G. {Constantin} and L. D. {Stefan} and B. {Ionescu} and C. {Demarty} and M. {Sjoberg} and M. {Schedl} and G. {Gravier}},
 journal={IEEE Transactions on Affective Computing},
 nodoi = {10.1109/TAFFC.2020.2986969},
 pages = {1-1},
 title = {Affect in Multimedia: Benchmarking Violent Scenes Detection},
 year = {2020}
}

@inproceedings{9206663,
 author = {Wang, Kaige and Xiong, Qingyu and Wu, Chao and Gao, Min and Yu, Yang},
  booktitle={International Joint Conference on Neural Networks (IJCNN)},
 nodoi = {10.1109/IJCNN48605.2020.9206663},
 pages = {1-8},
 title = {Multi-modal cyberbullying detection on social networks},
 year = {2020}
}

@inproceedings{AAAI2018:factchecking,
 author = {Tsvetomila Mihaylova and
Preslav Nakov and
Llu{\'{\i}}s M{\`{a}}rquez and
Alberto Barr{\'{o}}n{-}Cede{\~{n}}o and
Mitra Mohtarami and
Georgi Karadzhov and
James R. Glass},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/MihaylovaNMBMKG18.bib},
 booktitle = {Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence,
(AAAI-18), the 30th innovative Applications of Artificial Intelligence
(IAAI-18), and the 8th {AAAI} Symposium on Educational Advances in
Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February
2-7, 2018},
 editor = {Sheila A. McIlraith and
Kilian Q. Weinberger},
 pages = {5309--5316},
 publisher = {{AAAI} Press},
 timestamp = {Mon, 22 Oct 2018 01:00:00 +0200},
 title = {Fact Checking in Community Forums},
 url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16780},
 year = {2018}
}

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@article{abdul2020mega,
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@inproceedings{ACL2016:trolls,
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@manual{ACMIdentityStandards,
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}

@inproceedings{Agichtein:2008:FHC:1341531.1341557,
 author = {Eugene Agichtein and
Carlos Castillo and
Debora Donato and
Aristides Gionis and
Gilad Mishne},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/wsdm/AgichteinCDGM08.bib},
 booktitle = {Proceedings of the International Conference on Web Search and Web
Data Mining, {WSDM} 2008, Palo Alto, California, USA, February 11-12,
2008},
 doi = {10.1145/1341531.1341557},
 editor = {Marc Najork and
Andrei Z. Broder and
Soumen Chakrabarti},
 pages = {183--194},
 publisher = {{ACM}},
 timestamp = {Tue, 06 Nov 2018 00:00:00 +0100},
 title = {Finding high-quality content in social media},
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@inproceedings{agrawal2017multimodal,
 title={Multimodal detection of fake social media use through a fusion of classification and pairwise ranking systems},
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}

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}

@article{ahmed2020covid,
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 publisher = {JMIR Publications Inc., Toronto, Canada},
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 year = {2020}
}

@book{Aho:72,
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 year = {1972}
}

@article{akhtar2020intense,
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 pages = {64--75},
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}



@article{albaum1997likert,
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 booktitle = {Proceedings of the 17th Annual Conference of the International Speech Communication Association},
 pages = {2934--2938},
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 title = {Automatic dialect detection in {A}rabic broadcast speech},
 year = {2016}
}

@article{allcott2017social,
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@article{allport1947psychology,
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@article{allwood2007mumin,
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 year = {2007}
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@inproceedings{Almela:2012:STD:2388616.2388619,
 address = {Avignon, France},
 author = {Almela, {\'A}ngela  and
Valencia-Garc{\'\i}a, Rafael  and
Cantos, Pascual},
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 pages = {15--22},
 publisher = {Association for Computational Linguistics},
 title = {Seeing through Deception: A Computational Approach to Deceit Detection in Written Communication},
 url = {https://www.aclweb.org/anthology/W12-0403},
 year = {2012}
}

@article{alqurashi2020large,
 author = {Sarah Alqurashi and Ahmad Alhindi and Eisa Alanazi},
 journal = {arXiv/2004.04315},
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 title = {Large {A}rabic {T}witter Dataset on {COVID}-19},
 year = {2020}
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@article{alshaabi2020world,
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 journal = {arXiv preprint arXiv:2003.12614},
 title = {How the world's collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter},
 year = {2020}
}

@article{alzanin2018detecting,
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 volume = {142},
 year = {2018}
}

@manual{Amsthm15,
 note = {\url{http://www.ctan.org/pkg/amsthm}},
 organization = {American Mathematical Society},
 title = {Using the amsthm Package}
}

@article{Ando2005,
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 year = {2005}
}

@inproceedings{andrew2007scalable,
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 year = {2007}
}

@inproceedings{angiani2018image,
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 pages = {159--164},
 title = {Image-Based Hoax Detection},
 year = {2018}
}

@inproceedings{antoun2020arabert,
 address = {Marseille, France},
 author = {Antoun, Wissam  and
Baly, Fady  and
Hajj, Hazem},
 booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection},
 isbn = {979-10-95546-51-1},
 language = {English},
 pages = {9--15},
 publisher = {European Language Resource Association},
 title = {{A}ra{BERT}: Transformer-based Model for {A}rabic Language Understanding},
 url = {https://www.aclweb.org/anthology/2020.osact-1.2},
 year = {2020}
}

@book{APA:83,
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@inproceedings{arevalo2017gated,
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 year = {2017}
}

@inproceedings{arora2019multitask,
 author = {Udit Arora and
William Scott Paka and
Tanmoy Chakraborty},
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2019},
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@article{article_faris_etal,
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@article{article_john,
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@article{article_kadir,
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@article{article_simon_etal,
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 year = {2004}
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@article{artstein2008inter,
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@article{atanasova2019automatic,
 author = {Atanasova, Pepa and Nakov, Preslav and M{\`a}rquez, Llu{\'\i}s and Barr{\'o}n-Cede{\~n}o, Alberto and Karadzhov, Georgi and Mihaylova, Tsvetomila and Mohtarami, Mitra and Glass, James},
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 year = {2019}
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@inproceedings{audebert:hal-02163257,
 address = {Toulouse, France},
 author = {Audebert, Nicolas and Herold, Catherine and Slimani, Kuider and Vidal, C{\'e}dric},
 booktitle = {{Conf{\'e}rence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA)}},
 hal_id = {hal-02163257},
 hal_version = {v1},
 keywords = {Document classification ; multimodal learning ; deep learning ; data fusion ; apprentissage profond ; fusion de donn{\'e}es ; apprentissage multimodal ; classification de documents},
 pdf = {https://hal.archives-ouvertes.fr/hal-02163257/file/article_apia.pdf},
 title = {{Multimodal deep networks for text and image-based document classification}},
 url = {https://hal.archives-ouvertes.fr/hal-02163257},
 year = {2019}
}

@inproceedings{augenstein-etal-2019-multifc,
 address = {Hong Kong, China},
 author = {Augenstein, Isabelle  and
Lioma, Christina  and
Wang, Dongsheng  and
Chaves Lima, Lucas  and
Hansen, Casper  and
Hansen, Christian  and
Simonsen, Jakob Grue},
 booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
 doi = {10.18653/v1/D19-1475},
 pages = {4685--4697},
 publisher = {Association for Computational Linguistics},
 title = {{M}ulti{FC}: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims},
 url = {https://www.aclweb.org/anthology/D19-1475},
 year = {2019}
}

@inproceedings{ba2016vera,
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 year = {2016}
}

@inproceedings{Ba:2016:VERA,
 acmid = {2890536},
 address = {Montr\&\#233;al, Qu\&\#233;bec, Canada},
 author = {Ba, Mouhamadou Lamine and Berti-Equille, Laure and Shah, Kushal and Hammady, Hossam M.},
 booktitle = {Proceedings of the 25th International Conference Companion on World Wide Web},
 isbn = {978-1-4503-4144-8},
 keywords = {data veracity, fact-checking, source trustworthiness, truth discovery},
 noaddress = {Republic and Canton of Geneva, Switzerland},
 nopublisher = {International World Wide Web Conferences Steering Committee},
 numpages = {4},
 pages = {159--162},
 series = {WWW '16 Companion},
 title = {{VERA}: A Platform for Veracity Estimation over Web Data},
 year = {2016}
}

@inproceedings{Baccianella:2009qd,
 address = {Pisa, Italy},
 author = {Baccianella, Stefano and Esuli, Andrea and Sebastiani, Fabrizio},
 booktitle = {Proceedings of the 9th IEEE International Conference on Intelligent Systems Design and Applications},
 date-added = {2015-07-01 12:48:04 +0000},
 date-modified = {2015-07-01 12:48:04 +0000},
 pages = {283--287},
 series = {ISDA~'09},
 title = {Evaluation Measures for Ordinal Regression},
 year = {2009}
}

@inproceedings{Baccianella:2009qd,
 address = {Pisa, Italy},
 author = {Baccianella, Stefano and Esuli, Andrea and Sebastiani, Fabrizio},
 booktitle = {Proceedings of the 9th IEEE International Conference on Intelligent Systems Design and Applications},
 date-added = {2015-07-01 12:48:04 +0000},
 date-modified = {2015-07-01 12:48:04 +0000},
 pages = {283--287},
 series = {ISDA~'09},
 title = {Evaluation Measures for Ordinal Regression},
 year = {2009}
}

@inproceedings{Baccianella:2009qd,
 address = {Pisa, Italy},
 author = {Baccianella, Stefano and Esuli, Andrea and Sebastiani, Fabrizio},
 booktitle = {Proceedings of the 9th IEEE International Conference on Intelligent Systems Design and Applications},
 date-added = {2015-07-01 12:48:04 +0000},
 date-modified = {2015-07-01 12:48:04 +0000},
 pages = {283--287},
 series = {ISDA~'09},
 title = {Evaluation Measures for Ordinal Regression},
 year = {2009}
}

@inproceedings{bachman2014learning,
 author = {Philip Bachman and
Ouais Alsharif and
Doina Precup},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/BachmanAP14.bib},
 booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference
on Neural Information Processing Systems 2014, December 8-13 2014,
Montreal, Quebec, Canada},
 editor = {Zoubin Ghahramani and
Max Welling and
Corinna Cortes and
Neil D. Lawrence and
Kilian Q. Weinberger},
 pages = {3365--3373},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Learning with Pseudo-Ensembles},
 url = {https://proceedings.neurips.cc/paper/2014/hash/66be31e4c40d676991f2405aaecc6934-Abstract.html},
 year = {2014}
}

@article{Baeza-Yates:2018:BW:3229066.3209581,
 acmid = {3209581},
 author = {Baeza-Yates, Ricardo},
 doi = {10.1145/3209581},
 issn = {0001-0782},
 issue_date = {June 2018},
 journal = {Commun. ACM},
 noaddress = {New York, NY, USA},
 nopublisher = {ACM},
 nourl = {http://doi.acm.org/10.1145/3209581},
 number = {6},
 numpages = {8},
 pages = {54--61},
 title = {Bias on the Web},
 volume = {61},
 year = {2018}
}

@inproceedings{balchev-EtAl:2016:SemEval,
 address = {San Diego, California},
 author = {Balchev, Daniel  and
Kiprov, Yasen  and
Koychev, Ivan  and
Nakov, Preslav},
 booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)},
 doi = {10.18653/v1/S16-1130},
 pages = {844--850},
 publisher = {Association for Computational Linguistics},
 title = {{PMI}-cool at {S}em{E}val-2016 Task 3: Experiments with {PMI} and Goodness Polarity Lexicons for Community Question Answering},
 url = {https://www.aclweb.org/anthology/S16-1130},
 year = {2016}
}

@article{balmau2018limiting,
 author = {Balmau, Oana and Guerraoui, Rachid and Kermarrec, Anne-Marie and Maurer, Alexandre and Pavlovic, Matej and Zwaenepoel, Willy},
 journal = {arXiv preprint arXiv:1808.09922},
 title = {Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness},
 year = {2018}
}

@inproceedings{baly-EtAl:2018:N18-2,
 address = {New Orleans, Louisiana},
 author = {Baly, Ramy  and
Mohtarami, Mitra  and
Glass, James  and
M{\`a}rquez, Llu{\'\i}s  and
Moschitti, Alessandro  and
Nakov, Preslav},
 booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
 doi = {10.18653/v1/N18-2004},
 pages = {21--27},
 publisher = {Association for Computational Linguistics},
 title = {Integrating Stance Detection and Fact Checking in a Unified Corpus},
 url = {https://www.aclweb.org/anthology/N18-2004},
 year = {2018},
 series = "ACL~'18",
}

@inproceedings{baly2018integrating,
 address = {New Orleans, Louisiana},
 author = {Baly, Ramy  and
Mohtarami, Mitra  and
Glass, James  and
M{\`a}rquez, Llu{\'\i}s  and
Moschitti, Alessandro  and
Nakov, Preslav},
 booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
 doi = {10.18653/v1/N18-2004},
 pages = {21--27},
 publisher = {Association for Computational Linguistics},
 title = {Integrating Stance Detection and Fact Checking in a Unified Corpus},
 url = {https://www.aclweb.org/anthology/N18-2004},
 year = {2018}
}

@inproceedings{baly2018predicting,
 address = {Brussels, Belgium},
 author = {Baly, Ramy  and
Karadzhov, Georgi  and
Alexandrov, Dimitar  and
Glass, James  and
Nakov, Preslav},
 booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D18-1389},
 pages = {3528--3539},
 publisher = {Association for Computational Linguistics},
 title = {Predicting Factuality of Reporting and Bias of News Media Sources},
 url = {https://www.aclweb.org/anthology/D18-1389},
 year = {2018}
}

@inproceedings{baly2019multi,
 address = {Minneapolis, Minnesota},
 author = {Baly, Ramy  and
Karadzhov, Georgi  and
Saleh, Abdelrhman  and
Glass, James  and
Nakov, Preslav},
 booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
 doi = {10.18653/v1/N19-1216},
 pages = {2109--2116},
 publisher = {Association for Computational Linguistics},
 title = {Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media},
 url = {https://www.aclweb.org/anthology/N19-1216},
 year = {2019}
}

@inproceedings{baly2020arabert,
 address = {Marseille, France},
 author = {Antoun, Wissam  and
Baly, Fady  and
Hajj, Hazem},
 booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection},
 isbn = {979-10-95546-51-1},
 language = {English},
 pages = {9--15},
 publisher = {European Language Resource Association},
 title = {{A}ra{BERT}: Transformer-based Model for {A}rabic Language Understanding},
 url = {https://www.aclweb.org/anthology/2020.osact-1.2},
 year = {2020}
}

@article{Banda:2020,
 author = {Juan M Banda and Ramya Tekumalla and Guanyu Wang and Jingyuan Yu and Tuo Liu and Yuning Ding and Gerardo Chowell},
 journal = {arXiv:2004.03688},
 title = {A large-scale {COVID}-19 {T}witter chatter dataset for open scientific research -- an international collaboration},
 year = {2020}
}

@inproceedings{banerjee-han:2009:NAACLHLT09-Short,
 address = {Boulder, Colorado},
 author = {Banerjee, Protima  and
Han, Hyoil},
 booktitle = {Proceedings of Human Language Technologies: The 2009 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers},
 pages = {157--160},
 publisher = {Association for Computational Linguistics},
 title = {Answer Credibility: A Language Modeling Approach to Answer Validation},
 url = {https://www.aclweb.org/anthology/N09-2040},
 year = {2009}
}

@inproceedings{bansal2021,
 author = {William Scott Paka and Rachit Bansal and Nidhi and Shubhashis Sengupta and Tanmoy Chakraborty},
 booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 title = {{Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets}},
  pages={188--200},
  year={2021},
  organization={Springer}
}

@inproceedings{barron2019proppy,
 author = {Alberto Barr{\'{o}}n{-}Cede{\~{n}}o and
Giovanni Da San Martino and
Israa Jaradat and
Preslav Nakov},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/Barron-CedenoMJ19.bib},
 booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
2019, The Thirty-First Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
USA, January 27 - February 1, 2019},
 doi = {10.1609/aaai.v33i01.33019847},
 pages = {9847--9848},
 publisher = {{AAAI} Press},
 timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
 title = {Proppy: {A} System to Unmask Propaganda in Online News},
 url = {https://doi.org/10.1609/aaai.v33i01.33019847},
 year = {2019}
}

@article{BARRONCEDENO20191849,
 author = {Barr{\'o}n-Cedeno, Alberto and Jaradat, Israa and Da San Martino, Giovanni and Nakov, Preslav},
 journal = {In IPM},
 number = {5},
 pages = {1849--1864},
 publisher = {Elsevier},
 title = {Proppy: Organizing the news based on their propagandistic content},
 volume = {56},
 year = {2019}
}

@article{basiri2021abcdm,
 author = {Basiri, Mohammad Ehsan and Nemati, Shahla and Abdar, Moloud and Cambria, Erik and Acharya, U Rajendra},
 journal = {Future Generation Computer Systems},
 pages = {279--294},
 publisher = {Elsevier},
 title = {{ABCDM}: An attention-based bidirectional CNN-RNN deep model for sentiment analysis},
 volume = {115},
 year = {2021}
}

@article{basnet2014learning,
 author = {Basnet, Ram B and Sung, Andrew H and Liu, Quingzhong},
 journal = {International Journal of Research in Engineering and Technology},
 number = {6},
 pages = {11--24},
 title = {Learning to detect phishing {URLs}},
 volume = {3},
 year = {2014}
}

@inproceedings{basu2019identification,
 author = {Basu, Kaustav},
 booktitle = {Companion Proceedings of The 2019 World Wide Web Conference},
 pages = {7--11},
 title = {Identification of the Source (s) of Misinformation Propagation Utilizing Identifying Codes},
 year = {2019}
}

@article{Baxter1997,
 abstract = {A Bayesian model of learning to learn by sampling from multiple tasks is presented. The multiple tasks are themselves generated by sampling from a distribution over an environment of related tasks. Such an environment is shown to be naturally modelled within a Bayesian context by the concept of an objective prior distribution. It is argued that for many common machine learning problems, although in general we do not know the true (objective) prior for the problem, we do have some idea of a set of possible priors to which the true prior belongs. It is shown that under these circumstances a learner can use Bayesian inference to learn the true prior by learning sufficiently many tasks from the environment. In addition, bounds are given on the amount of information required to learn a task when it is simultaneously learnt with several other tasks. The bounds show that if the learner has little knowledge of the true prior, but the dimensionality of the true prior is small, then sampling multiple tasks is highly advantageous. The theory is applied to the problem of learning a common feature set or equivalently a low-dimensional-representation (LDR) for an environment of related tasks.},
 author = {Baxter, Jonathan},
 day = {01},
 issn = {1573-0565},
 journal = {Machine Learning},
 nodoi = {10.1023/A:1007327622663},
 nourl = {https://doi.org/10.1023/A:1007327622663},
 number = {1},
 pages = {7--39},
 title = {A {B}ayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling},
 volume = {28},
 year = {1997}
}

@inproceedings{bayar2016deep,
 author = {Bayar, Belhassen and Stamm, Matthew C},
 booktitle = {Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security},
 pages = {5--10},
 title = {A deep learning approach to universal image manipulation detection using a new convolutional layer},
 year = {2016}
}

@inproceedings{Berger:2000:BLC:345508.345576,
 author = {Berger, Adam and Caruana, Rich and Cohn, David and Freitag, Dayne and Mittal, Vibhu},
 booktitle = {Proceedings of SIGIR '00},
 pages = {192--199},
 title = {Bridging the Lexical Chasm: Statistical Approaches to Answer-finding},
 year = {2000}
}

@inproceedings{bergstra+al:2010-scipy,
 address = {Austin, Texas},
 author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Desjardins, Guillaume and Turian, Joseph and Warde-Farley, David and Bengio, Yoshua},
 booktitle = {Proceedings of the Python for Scientific Computing Conference},
 series = {SciPy~'10},
 title = {Theano: a {CPU} and {GPU} Math Expression Compiler},
 year = {2010}
}

@inproceedings{bert,
 address = {Minneapolis, Minnesota},
 author = {Devlin, Jacob  and
Chang, Ming-Wei  and
Lee, Kenton  and
Toutanova, Kristina},
 booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
 doi = {10.18653/v1/N19-1423},
 pages = {4171--4186},
 publisher = {Association for Computational Linguistics},
 title = {{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding},
 url = {https://www.aclweb.org/anthology/N19-1423},
 year = {2019}
}

@inproceedings{bgf:Lixto,
 address = {Rome, Italy},
 author = {Robert Baumgartner and Georg Gottlob and Sergio Flesca},
 booktitle = {Proceedings of the 27th International Conference on Very Large Databases},
 pages = {119--128},
 publisher = {Morgan Kaufmann},
 title = {Visual Information Extraction with {Lixto}},
 year = {2001}
}

@article{bhardwaj2020hostility,
 author = {Bhardwaj, Mohit and Akhtar, Md Shad and Ekbal, Asif and Das, Amitava and Chakraborty, Tanmoy},
 journal = {arXiv preprint arXiv:2011.03588},
 title = {Hostility detection dataset in hindi},
 year = {2020}
}

@book{Bird:2009:NLP:1717171,
 author = {Bird, Steven and Klein, Ewan and Loper, Edward},
 edition = {1st},
 isbn = {0596516495, 9780596516499},
 publisher = {O'Reilly Media, Inc.},
 title = {Natural Language Processing with Python},
 year = {2009}
}

@inproceedings{blei2003latent,
 author = {David M. Blei and
Andrew Y. Ng and
Michael I. Jordan},
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 booktitle = {Advances in Neural Information Processing Systems 14 [Neural Information
Processing Systems: Natural and Synthetic, {NIPS} 2001, December 3-8,
2001, Vancouver, British Columbia, Canada]},
 editor = {Thomas G. Dietterich and
Suzanna Becker and
Zoubin Ghahramani},
 pages = {601--608},
 publisher = {{MIT} Press},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Latent Dirichlet Allocation},
 url = {https://proceedings.neurips.cc/paper/2001/hash/296472c9542ad4d4788d543508116cbc-Abstract.html},
 year = {2001}
}

@inproceedings{boididou2014challenges,
 author = {Boididou, Christina and Papadopoulos, Symeon and Kompatsiaris, Yiannis and Schifferes, Steve and Newman, Nic},
 booktitle = {Proceedings of the 23rd International Conference on World Wide Web},
 pages = {743--748},
 title = {Challenges of computational verification in social multimedia},
 year = {2014}
}

@inproceedings{boididou2015certh,
 author = {Boididou, Christina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Kompatsiaris, Yiannis},
 booktitle = {MediaEval},
 title = {The CERTH-UNITN Participation@ Verifying Multimedia Use 2015.},
 year = {2015}
}

@article{boididou2015verifying,
 author = {Boididou, Christina and Andreadou, Katerina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Kompatsiaris, Yiannis and others},
 journal = {MediaEval},
 number = {3},
 pages = {7},
 title = {Verifying Multimedia Use at {MediaEval} 2015},
 volume = {3},
 year = {2015}
}

@article{boididou2015verifying1,
 author = {Boididou, Christina and Andreadou, Katerina and Papadopoulos, Symeon and Dang-Nguyen, Duc-Tien and Boato, Giulia and Riegler, Michael and Kompatsiaris, Yiannis and others},
 journal = {MediaEval},
 number = {3},
 pages = {7},
 title = {Verifying Multimedia Use at MediaEval 2015.},
 volume = {3},
 year = {2015}
}

@article{boididou2016verifying,
 author = {Boididou, Christina and Papadopoulos, Symeon and Middleton, Stuart E. and Boato, Giulia and Dang-Nguyen, Duc-Tien and Riegler, Michael},
 journal = {MediaEval},
     title = {Verifying multimedia use at {MediaEval} 2016},
 year = {2016}
}

@article{boididou2018detection,
 author = {Detection and visualization of misleading content on Twitter},
 doi = {10.1007/s13735-017-0143-x},
 journal = {International Journal of Multimedia Information Retrieval},
 number = {1},
 pages = {71--86},
 publisher = {Springer},
 title = {Boididou, Christina and Papadopoulos, Symeon and Zampoglou, Markos and Apostolidis, Lazaros and Papadopoulou, Olga and Kompatsiaris, Yiannis},
 volume = {7},
 year = {2018}
}

@article{bojanowski2017enriching,
 author = {Bojanowski, Piotr  and
Grave, Edouard  and
Joulin, Armand  and
Mikolov, Tomas},
 doi = {10.1162/tacl_a_00051},
 journal = {Transactions of the Association for Computational Linguistics},
 pages = {135--146},
 title = {Enriching Word Vectors with Subword Information},
 url = {https://www.aclweb.org/anthology/Q17-1010},
 volume = {5},
 year = {2017}
}

@article{bondielli2019survey,
 author = {Bondielli, Alessandro and Marcelloni, Francesco},
 journal = {Information Sciences},
 pages = {38--55},
 publisher = {Elsevier},
 title = {A survey on fake news and rumour detection techniques},
 volume = {497},
 year = {2019}
}

@book{book_holocaust,
 author = {Facing History and Ourselves},
 publisher = {Facing History and Ourselves},
 title = {Holocaust and Human Behavior},
 year = {2017}
}

@book{book_prop,
 author = {Institute for Propaganda Analysis},
 publisher = {Institute for Propaganda Analysis, Inc},
 title = {Propaganda Analysis: Volume II of the Publications of the Institute for Propaganda Analysis},
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}

@inproceedings{borsch2011,
 address = {Canberra, Australia},
 author = {B{\"o}rschinger, Benjamin  and
Johnson, Mark},
 booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2011},
 pages = {10--18},
 title = {A Particle Filter algorithm for {B}ayesian Wordsegmentation},
 url = {https://www.aclweb.org/anthology/U11-1004},
 year = {2011}
}

@article{bovet2019influence,
 author = {Bovet, Alexandre and Makse, Hern{\'a}n A},
 journal = {Nature communications},
 number = {1},
 pages = {1--14},
 publisher = {Nature Publishing Group},
 title = {Influence of fake news in Twitter during the 2016 US presidential election},
 volume = {10},
 year = {2019}
}

@article{bowman:reasoning,
 author = {Bowman, Mic and Debray, Saumya K. and Peterson, Larry L.},
 doi = {10.1145/161468.161471},
 journal = {ACM Trans. Program. Lang. Syst.},
 number = {5},
 pages = {795-825},
 title = {Reasoning About Naming Systems},
 volume = {15},
 year = {1993}
}

@inproceedings{bozarth2020toward,
 author = {Bozarth, Lia and Budak, Ceren},
 booktitle={Proceedings of the international AAAI conference on web and social media},
 pages = {60--71},
 title = {Toward a better performance evaluation framework for fake news classification},
 volume = {14},
 series = {AAAI~'20},
 year = {2020}
}

@article{braams:babel,
 author = {Braams, Johannes},
 journal = {TUGboat},
 number = {2},
 pages = {291-301},
 title = {Babel, a Multilingual Style-Option System for Use with LaTeX's Standard Document Styles},
 volume = {12},
 year = {1991}
}

@article{brachman-schmolze:kl-one,
 author = {Ronald~J. Brachman and James~G. Schmolze},
 journal = {Cognitive Science},
 number = {2},
 pages = {171--216},
 title = {An overview of the {KL-ONE} knowledge representation system},
 volume = {9},
 year = {1985}
}

@article{brank2018semantic,
 author = {Brank, Janez and Leban, Gregor and Grobelnik, Marko},
 journal = {Informatica},
 number = {1},
 title = {Semantic Annotation of Documents Based on Wikipedia Concepts},
 volume = {42},
 year = {2018}
}

@article{Brasoveanu2020,
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 eprint = {arXiv:1909.03654v1},
 issn = {16130073},
 journal = {CEUR Workshop Proceedings},
 keywords = {AI for social good,Language models,Sentiment classification,State-managed medical review processes,Topic models},
 number = {1},
 pages = {1--9},
 title = {Textual evidence for the perfunctoriness of independent medical reviews},
 volume = {2657},
 year = {2020}
}

@incollection{brauer2008introduction,
 author = {Brauer, Fred},
 booktitle = {Mathematical epidemiology},
 pages = {133--146},
 publisher = {Springer},
 title = {An introduction to networks in epidemic modeling},
 year = {2008}
}

@article{Brennen2020,
 author = {Brennen, J and Simon, Felix and Howard, Philip and Nielsen, Rasmus},
 pages = {},
 title = {Types, Sources, and Claims of {COVID-19} Misinformation},
 year = {2020}
}

@article{brill2001online,
 author = {Brill, Ann M},
 journal = {Newspaper Research Journal},
 number = {2},
 pages = {28},
 publisher = {Newspaper Research Journal, Department of Journalism, University of Memphis},
 title = {Online journalists embrace new marketing function},
 volume = {22},
 year = {2001}
}

@manual{Brito09,
 annote = {\url{http://www.ctan.org/pkg/algorithms}},
 author = {Rog\'erio Brito},
 title = {The algorithms bundle}
}

@article{broniatowski2018weaponized,
 author = {Broniatowski, David A and Jamison, Amelia M and Qi, SiHua and AlKulaib, Lulwah and Chen, Tao and Benton, Adrian and Quinn, Sandra C and Dredze, Mark},
 journal = {American journal of public health},
 number = {10},
 pages = {1378--1384},
 publisher = {American Public Health Association},
 title = {Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate},
 volume = {108},
 year = {2018}
}

@article{Brown:1993:MSM,
 author = {Brown, Peter F.  and
Della Pietra, Stephen A.  and
Della Pietra, Vincent J.  and
Mercer, Robert L.},
 journal = {Computational Linguistics},
 number = {2},
 pages = {263--311},
 title = {The Mathematics of Statistical Machine Translation: Parameter Estimation},
 url = {https://www.aclweb.org/anthology/J93-2003},
 volume = {19},
 year = {1993}
}

@inproceedings{btEdunov,
 address = {Brussels, Belgium},
 author = {Edunov, Sergey  and
Ott, Myle  and
Auli, Michael  and
Grangier, David},
 booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D18-1045},
 pages = {489--500},
 publisher = {Association for Computational Linguistics},
 title = {Understanding Back-Translation at Scale},
 url = {https://www.aclweb.org/anthology/D18-1045},
 year = {2018}
}

@article{Bu:2013:SPD:2400768.2401510,
 acmid = {2401510},
 address = {Amsterdam, The Netherlands, The Netherlands},
 author = {Bu, Zhan and Xia, Zhengyou and Wang, Jiandong},
 issn = {0950-7051},
 issue_date = {2013},
 journal = {Know.-Based Syst.},
 keywords = {Authorship identification, Community detection, Hypothesis test, Sock puppet, Virtual spaces},
 nodoi = {10.1016/j.knosys.2012.08.016},
 numpages = {12},
 pages = {366--377},
 publisher = {Elsevier Science Publishers B. V.},
 title = {A sock puppet detection algorithm on virtual spaces},
 volume = {37},
 year = {2013}
}

@inproceedings{Buckley:2000,
 address = {Athens, Greece},
 author = {Buckley, Chris and Voorhees, Ellen M.},
 booktitle = {Proceedings of the 23rd Annual International Conference on Research and Development in Information Retrieval},
 noacmid = {345543},
 noaddress = {New York, NY, USA},
 nodoi = {10.1145/345508.345543},
 noisbn = {1-58113-226-3},
 nopublisher = {ACM},
 nourl = {http://doi.acm.org/10.1145/345508.345543},
 numpages = {8},
 pages = {33--40},
 series = {SIGIR '00},
 title = {Evaluating Evaluation Measure Stability},
 year = {2000}
}

@misc{buzzfeednews,
 author = {Singer-Vine, Jeremy},
 howpublished = {\url{https://github.com/BuzzFeedNews/2016-10-facebook-fact-check}},
 note = {2016},
 title = {BuzzFeedNews dataset}
}

@inproceedings{C14-1001,
 address = {Dublin, Ireland},
 author = {Harper, Mary},
 booktitle = {Proceedings of {COLING} 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
 pages = {1},
 publisher = {Dublin City University and Association for Computational Linguistics},
 title = {Learning from 26 Languages: Program Management and Science in the Babel Program},
 url = {https://www.aclweb.org/anthology/C14-1001},
 year = {2014}
}

@article{Calvo2008,
 author = {Calvo, P.C. and Escobar, A. and Pinedo, C.},
 isbn = {9781450355520},
 journal = {Revista Facultad de Ingenieria},
 keywords = {acm reference format,adversarial neural networks,deep learning,fake news detection,fenglong ma,guangxu xun,kishlay,yaqing wang,ye yuan,zhiwei jin},
 number = {46},
 pages = {90--100},
 title = {Electromagnetic interference on medical equipments due to wireless communication devices | Interferencia electromagn{\'{e}}tica en equipos m{\'{e}}dicos debida a equipos de comunicaci{\'{o}}n inal{\'{a}}mbrica},
 year = {2008}
}

@incollection{cambria2017affective,
 author = {Cambria, Erik and Das, Dipankar and Bandyopadhyay, Sivaji and Feraco, Antonio},
 booktitle = {A practical guide to sentiment analysis},
 pages = {1--10},
 publisher = {Springer},
 title = {Affective computing and sentiment analysis},
 year = {2017}
}

@article{cao2020exploring,
 author = {Cao, Juan and Qi, Peng and Sheng, Qiang and Yang, Tianyun and Guo, Junbo and Li, Jintao},
 journal = {arXiv preprint arXiv:2003.05096},
 title = {Exploring the Role of Visual Content in Fake News Detection},
 year = {2020}
}

@inproceedings{CardosoDurierdaSilva2019,
 author = {{Cardoso Durier da Silva}, Fernando and Vieira, Rafael and Garcia, Ana Cristina},
 booktitle={Proceedings of the 52nd Hawaii International Conference on System Sciences},
 keywords = {1,and synonyms on parsifal,as we can see,on},
 nodoi = {10.24251/hicss.2019.332},
 noisbn = {9780998133126},
 pages = {2763--2770},
 title = {{Can machines learn to detect fake news? A survey focused on social media}},
 volume = {6},
 year = {2019}
}

@article{carlmiller2020,
 author = {Carl Miller},
 journal = {https://www.bbc.com/news/technology-52490430},
 publisher = {BBC Click},
 title = {Coronavirus: Far-right spreads {COVID-19} `infodemic' on {F}acebook},
 urldate = {May 4th, 2020},
 year = {2020}
}

@inproceedings{Caruana93multitasklearning,
 address = {Amherst, MA, USA},
 author = {Richard Caruana},
 booktitle = {Proceedings of the Tenth International Conference on Machine Learning},
 nopublisher = {Morgan Kaufmann},
 pages = {41--48},
 series = {ICML~'13},
 title = {Multitask Learning: A Knowledge-Based Source of Inductive Bias},
 year = {1993}
}

@article{cassidy2007online,
 author = {Cassidy, William P},
 journal = {Journal of Computer-Mediated Communication},
 number = {2},
 pages = {478--498},
 publisher = {Wiley Online Library},
 title = {Online news credibility: An examination of the perceptions of newspaper journalists},
 volume = {12},
 year = {2007}
}

@inproceedings{castillo2011information,
 author = {Carlos Castillo and
Marcelo Mendoza and
Barbara Poblete},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/www/CastilloMP11.bib},
 booktitle = {Proceedings of the 20th International Conference on World Wide Web,
{WWW} 2011, Hyderabad, India, March 28 - April 1, 2011},
 doi = {10.1145/1963405.1963500},
 editor = {Sadagopan Srinivasan and
Krithi Ramamritham and
Arun Kumar and
M. P. Ravindra and
Elisa Bertino and
Ravi Kumar},
 pages = {675--684},
 publisher = {{ACM}},
 timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
 title = {Information credibility on twitter},
 url = {https://doi.org/10.1145/1963405.1963500},
 year = {2011}
}

@inproceedings{Castillo:2011:ICT:1963405.1963500,
 author = {Carlos Castillo and
Marcelo Mendoza and
Barbara Poblete},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/www/CastilloMP11.bib},
 booktitle = {Proceedings of the 20th International Conference on World Wide Web,
{WWW} 2011, Hyderabad, India, March 28 - April 1, 2011},
 doi = {10.1145/1963405.1963500},
 editor = {Sadagopan Srinivasan and
Krithi Ramamritham and
Arun Kumar and
M. P. Ravindra and
Elisa Bertino and
Ravi Kumar},
 pages = {675--684},
 publisher = {{ACM}},
 timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
 title = {Information credibility on twitter},
 url = {https://doi.org/10.1145/1963405.1963500},
 year = {2011}
}

@article{CC01a,
 author = {Chang, Chih-Chung and Lin, Chih-Jen},
 issue = {3},
 journal = {ACM Transactions on Intelligent Systems and Technology},
 note = {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}},
 pages = {27:1--27:27},
 title = {{LIBSVM}: A library for support vector machines},
 volume = {2},
 year = {2011}
}

@article{cer2018universal,
 author = {Cer, Daniel and Yang, Yinfei and Kong, Sheng-yi and Hua, Nan and Limtiaco, Nicole and John, Rhomni St and Constant, Noah and Guajardo-Cespedes, Mario and Yuan, Steve and Tar, Chris and others},
 journal = {arXiv:1803.11175},
 title = {Universal sentence encoder},
 year = {2018}
}

@article{CHAKRABORTY2020106754,
 abstract = {COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.},
 author = {Koyel Chakraborty and Surbhi Bhatia and Siddhartha Bhattacharyya and Jan Platos and Rajib Bag and Aboul Ella Hassanien},
 doi = {https://doi.org/10.1016/j.asoc.2020.106754},
 issn = {1568-4946},
 journal = {Applied Soft Computing},
 keywords = {Tweets, COVID-19, Sentiment analysis, Deep learning, Emotional intelligence, WHO, Fuzzy rule, Gaussian membership function},
 pages = {106754},
 title = {Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media},
 url = {http://www.sciencedirect.com/science/article/pii/S156849462030692X},
 volume = {97},
 year = {2020}
}

@article{Chandra:81,
 author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
 doi = {10.1145/322234.322243},
 journal = {Journal of the Association for Computing Machinery},
 number = {1},
 pages = {114--133},
 title = {Alternation},
 volume = {28},
 year = {1981}
}

@article{chapelle2009semi,
 author = {Chapelle, Olivier and Scholkopf, Bernhard and Zien, Alexander},
 journal = {IEEE TNN},
 number = {3},
 pages = {542--542},
 publisher = {IEEE},
 title = {Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]},
 volume = {20},
 year = {2009}
}

@article{chatzakou2019detecting,
 author = {Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Cristofaro, Emiliano De and Stringhini, Gianluca and Vakali, Athena and Kourtellis, Nicolas},
 journal={ACM Transactions on the Web (TWEB)},
 number = {3},
 pages = {1--51},
 publisher = {ACM},
 title = {Detecting cyberbullying and cyberaggression in social media},
 volume = {13},
 year = {2019}
}

@article{chawla2002smote,
 author = {Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
 journal = {Journal of artificial intelligence research},
 pages = {321--357},
 title = {SMOTE: synthetic minority over-sampling technique},
 volume = {16},
 year = {2002}
}

@inproceedings{checkthat19:Altun,
 address = {Lugano, Switzerland},
 author = {Altun, Bahadir and
Kutlu, Mucahid},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {{TOBB-ETU} at {CLEF} 2019: Prioritizing Claims Based on Check-Worthiness},
 year = {2019}
}

@inproceedings{checkthat19:Coca,
 address = {Lugano, Switzerland},
 author = {Coca, {Lucia Georgiana} and
Cusmuliuc, Ciprian-Gabriel and
Iftene, Adrian},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {2019 {UAICS}},
 year = {2019}
}

@inproceedings{checkthat19:Dhar,
 address = {Lugano, Switzerland},
 author = {Dhar, Rudra and
Dutta, Subhabrata and
Das, Dipankar},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {A Hybrid Model to Rank Sentences for Check-worthiness},
 year = {2019}
}

@inproceedings{checkthat19:Favano,
 address = {Lugano, Switzerland},
 author = {Favano, Luca and
Carman, {Mark J.} and
Lanzi, {Pier Luca}},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {{TheEarthIsFlat}'s Submission to {CLEF'19} {CheckThat}! Challenge},
 year = {2019}
}

@inproceedings{checkthat19:Gasior,
 address = {Lugano, Switzerland},
 author = {Gasior, Jakub and
Przyby{\l}a, Piotr },
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {The {IPIPAN} Team Participation in the Check-Worthiness Task of the {CLEF2019 CheckThat!} Lab},
 year = {2019}
}

@inproceedings{checkthat19:Ghanem,
 address = {Lugano, Switzerland},
 author = {Ghanem, Bilal and
Glavaš, Goran and
Giachanou, Anastasia and
Ponzetto, {Simone Paolo} and
Rosso, Paolo and
Rangel, Francisco},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {UPV-UMA at CheckThat! Lab: Verifying Arabic Claims using Cross Lingual Approach},
 year = {2019}
}

@inproceedings{checkthat19:Gąsior,
 address = {Lugano, Switzerland},
 author = {Gasior, Jakub and
Przyby{\l}a, Piotr },
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {The {IPIPAN} Team Participation in the Check-Worthiness Task of the {CLEF2019 CheckThat!} Lab},
 year = {2019}
}

@inproceedings{checkthat19:Hansen,
 address = {Lugano, Switzerland},
 author = {Hansen, Casper and
Hansen, Christian and
Simonsen, {Jakob Grue} and 
Lioma, Christina},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Neural Weakly Supervised Fact Check-Worthiness Detection with Contrastive Sampling-Based Ranking Loss},
 year = {2019}
}

@inproceedings{checkthat19:Haouari,
 address = {Lugano, Switzerland},
 author = {Haouari, Fatima and
Ali, {Zien Sheikh} and
Elsayed, Tamer},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {bigIR at CLEF 2019: Automatic Verification of Arabic Claims over the Web},
 year = {2019}
}

@inproceedings{checkthat19:Kumar,
 address = {Lugano, Switzerland},
 author = {Kumar, Ritesh and
Prakash, Shivansh and
Kumar, Shashank and Pamula, Rajendra},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Check That! Automatic Identification and Verification of Claims: IIT(ISM) @CLEF'19 Check Worthiness},
 year = {2019}
}

@inproceedings{checkthat19:Mohtaj,
 address = {Lugano, Switzerland},
 author = {Mohtaj, Salar and
Himmelsbach, Tilo and
Woloszyn, Vinicius and
Möller, Sebastian},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {The {TU-Berlin} Team Participation in the Check-Worthiness Task of the {CLEF-2019 CheckThat!} Lab},
 year = {2019}
}

@inproceedings{checkthat19:Su,
 address = {Lugano, Switzerland},
 author = {Su, Ting and
Macdonald, Craig and Ounis, Iadh},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Entity Detection for Check-worthiness Prediction: {Glasgow Terrier at CLEF CheckThat!} 2019},
 year = {2019}
}

@inproceedings{checkthat19:Touahri,
 address = {Lugano, Switzerland},
 author = {Touahri, Ibtissam and
Mazroui, Azzeddine },
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Automatic Identification and Verification of Political Claims},
 year = {2019}
}

@inproceedings{CheckThat:ECIR2019,
 author = {Elsayed, Tamer
and Nakov, Preslav
and Barr{\'o}n-Cede{\~{n}}o, Alberto
and Hasanain, Maram
and Suwaileh, Reem
and Da San Martino, Giovanni
and Atanasova, Pepa},
 booktitle = {Advances in Information Retrieval},
 editor = {Azzopardi, Leif
and Stein, Benno
and Fuhr, Norbert
and Mayr, Philipp
and Hauff, Claudia
and Hiemstra, Djoerd},
 isbn = {978-3-030-15719-7},
 noaddress = {Cham},
 pages = {309--315},
 publisher = {Springer International Publishing},
 series = {ECIR~'19},
 title = {{CheckThat! at CLEF 2019}: Automatic Identification and Verification of Claims},
 year = {2019}
}

@inproceedings{CheckThat:ECIR2020,
 address = {Lisbon, Portugal},
 author = {Alberto Barr{\'{o}}n{-}Cede{\~{n}}o and
Tamer Elsayed and
Preslav Nakov and
Giovanni Da San Martino and
Maram Hasanain and
Reem Suwaileh and
Fatima Haouari},
 booktitle = {Proc. of the 42nd ECIR},
 pages = {499--507},
 series = {ECIR~'19},
 title = {CheckThat! at {CLEF} 2020: Enabling the Automatic Identification and Verification of Claims in Social Media},
 year = {2020}
}

@inproceedings{chen2014predicting,
 author = {Chen et al., Yan-Ying},
 booktitle = {ICMR},
 title = {Predicting viewer affective comments based on image content in social media},
 year = {2014}
}

@inproceedings{chen2016enhanced,
 address = {Vancouver, Canada},
 author = {Chen, Qian  and
Zhu, Xiaodan  and
Ling, Zhen-Hua  and
Wei, Si  and
Jiang, Hui  and
Inkpen, Diana},
 booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
 doi = {10.18653/v1/P17-1152},
 pages = {1657--1668},
 publisher = {Association for Computational Linguistics},
 title = {Enhanced {LSTM} for Natural Language Inference},
 url = {https://www.aclweb.org/anthology/P17-1152},
 year = {2017}
}

@inproceedings{chen2018call,
 author = {Chen, Tong and Li, Xue and Yin, Hongzhi and Zhang, Jun},
 booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 organization = {Springer},
 pages = {40--52},
 title = {Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection},
 year = {2018}
}

@inproceedings{chen2018mix,
 author = {Haolan Chen and
Fred X. Han and
Di Niu and
Dong Liu and
Kunfeng Lai and
Chenglin Wu and
Yu Xu},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/kdd/ChenHNLLWX18.bib},
 booktitle = {Proceedings of the 24th {ACM} {SIGKDD} International Conference on
Knowledge Discovery {\&} Data Mining, {KDD} 2018, London, UK, August
19-23, 2018},
 doi = {10.1145/3219819.3219928},
 editor = {Yike Guo and
Faisal Farooq},
 pages = {110--119},
 publisher = {{ACM}},
 timestamp = {Wed, 21 Nov 2018 00:00:00 +0100},
 title = {{MIX:} Multi-Channel Information Crossing for Text Matching},
 url = {https://doi.org/10.1145/3219819.3219928},
 year = {2018}
}

@inproceedings{chen2019variational,
 address = {Brussels, Belgium},
 author = {Chen, Mingda  and
Tang, Qingming  and
Livescu, Karen  and
Gimpel, Kevin},
 booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D18-1020},
 pages = {215--226},
 publisher = {Association for Computational Linguistics},
 title = {Variational Sequential Labelers for Semi-Supervised Learning},
 url = {https://www.aclweb.org/anthology/D18-1020},
 year = {2018}
}

@inproceedings{chen2020mixtext,
 address = {Online},
 author = {Chen, Jiaao  and
Yang, Zichao  and
Yang, Diyi},
 booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
 doi = {10.18653/v1/2020.acl-main.194},
 pages = {2147--2157},
 publisher = {Association for Computational Linguistics},
 title = {{M}ix{T}ext: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification},
 url = {https://www.aclweb.org/anthology/2020.acl-main.194},
 year = {2020}
}

@inproceedings{Chen:2013:BIW:2492517.2492637,
 author = {Cheng Chen and
Kui Wu and
Srinivasan Venkatesh and
Xudong Zhang},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/asunam/ChenWSZ13.bib},
 booktitle = {Advances in Social Networks Analysis and Mining 2013, {ASONAM} '13,
Niagara, ON, Canada - August 25 - 29, 2013},
 doi = {10.1145/2492517.2492637},
 editor = {Jon G. Rokne and
Christos Faloutsos},
 pages = {116--120},
 publisher = {{ACM}},
 timestamp = {Mon, 20 Jan 2020 00:00:00 +0100},
 title = {Battling the internet water army: detection of hidden paid posters},
 url = {https://doi.org/10.1145/2492517.2492637},
 year = {2013}
}

@inproceedings{chetan2019corerank,
 author = {Aditya Chetan and
Brihi Joshi and
Hridoy Sankar Dutta and
Tanmoy Chakraborty},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/wsdm/ChetanJD019.bib},
 booktitle = {Proceedings of the Twelfth {ACM} International Conference on Web Search
and Data Mining, {WSDM} 2019, Melbourne, VIC, Australia, February
11-15, 2019},
 doi = {10.1145/3289600.3291010},
 editor = {J. Shane Culpepper and
Alistair Moffat and
Paul N. Bennett and
Kristina Lerman},
 pages = {330--338},
 publisher = {{ACM}},
 timestamp = {Sat, 19 Oct 2019 01:00:00 +0200},
 title = {CoReRank: Ranking to Detect Users Involved in Blackmarket-Based Collusive
Retweeting Activities},
 url = {https://doi.org/10.1145/3289600.3291010},
 year = {2019}
}

@article{cinelli2020covid,
 author = {Cinelli, Matteo and Quattrociocchi, Walter and Galeazzi, Alessandro and Valensise, Carlo Michele and Brugnoli, Emanuele and Schmidt, Ana Lucia and Zola, Paola and Zollo, Fabiana and Scala, Antonio},
 journal = {arXiv preprint arXiv:2003.05004},
 title = {The covid-19 social media infodemic},
 year = {2020}
}

@article{cinelli2020covid19,
 author = {Matteo Cinelli and Walter Quattrociocchi and Alessandro Galeazzi and Carlo Michele Valensise and Emanuele Brugnoli and Ana Lucia Schmidt and Paola Zola and Fabiana Zollo and Antonio Scala},
 journal = {arXiv:2003.05004},
 primaryclass = {cs.SI},
 title = {The {COVID}-19 Social Media Infodemic},
 year = {2020}
}

@inproceedings{ClaimsKG,
 address = {Auckland, New Zealand},
 author = {Andon Tchechmedjiev and
Pavlos Fafalios and
Katarina Boland and
Malo Gasquet and
Matth{\"{a}}us Zloch and
Benjamin Zapilko and
Stefan Dietze and
Konstantin Todorov},
 booktitle = {Proc. of the 18th International Semantic Web Conference},
 pages = {309--324},
 series = {ISWC~'19},
 title = {{ClaimsKG}: A Knowledge Graph of Fact-Checked Claims},
 year = {2019}
}

@inproceedings{clark2016improving,
 address = {Berlin, Germany},
 author = {Clark, Kevin  and
Manning, Christopher D.},
 booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
 doi = {10.18653/v1/P16-1061},
 pages = {643--653},
 publisher = {Association for Computational Linguistics},
 title = {Improving Coreference Resolution by Learning Entity-Level Distributed Representations},
 url = {https://www.aclweb.org/anthology/P16-1061},
 year = {2016}
}

@inproceedings{clark2018semi,
 address = {Brussels, Belgium},
 author = {Clark, Kevin  and
Luong, Minh-Thang  and
Manning, Christopher D.  and
Le, Quoc},
 booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D18-1217},
 pages = {1914--1925},
 publisher = {Association for Computational Linguistics},
 title = {Semi-Supervised Sequence Modeling with Cross-View Training},
 url = {https://www.aclweb.org/anthology/D18-1217},
 year = {2018}
}

@inproceedings{clark:pct,
 author = {Malcolm Clark},
 booktitle = {TeX90 Conference Proceedings},
 organization = {TeX Users Group},
 pages = {84-89},
 title = {Post Congress Tristesse},
 year = {1991}
}

@inproceedings{clark:pct,
 author = {Malcolm Clark},
 booktitle = {TeX90 Conference Proceedings},
 organization = {TeX Users Group},
 pages = {84-89},
 title = {Post Congress Tristesse},
 year = {1991}
}

@inproceedings{clark:pct,
 author = {Malcolm Clark},
 booktitle = {TeX90 Conference Proceedings},
 organization = {TeX Users Group},
 pages = {84-89},
 title = {Post Congress Tristesse},
 year = {1991}
}

@inproceedings{clef-checkthat-Alkhalifa:2020,
 abstract = {This paper describes the QMUL-SDS team's entry for Task 1 of the 
CLEF 2020 CheckThat! shared task, where we determined the check-worthiness of 
tweets about COVID-19 to prioritise tweets for fact-checking. We utilised a BERT 
language model with binary classification and sigmoid for ranking. In 
particular, we used the uncased COVID-Twitter-BERT architecture that had been 
pre-trained on COVID-19 Twitter stream data using two different language 
modelling tasks: whole word-masked modelling and next sentence prediction. The 
model was trained by jointly optimising a binary cross-entropy loss. We 
performed traditional Twitter data preprocessing in order to improve system 
performance: (1) All Twitter account handles, digits, hashtags and hyperlinks 
were removed; (2) A vector look-up was then used for each token from CO-BERT 
model; (3) Each token was thereafter mapped to a unique integer in the corpus’ 
vocabulary, done using two tokenisers which performed differently in different 
cases; (4) Padding was used to equalise the lengths of the input sequences in a 
batch, i.e. we increase the length of some sequences by adding tokens. We 
attempted to reduce the padding by letting our model decide the padding length 
based on the given batch and its longest sequence, reducing any unnecessary 
overhead carried by the model. We ranked fourth in the task.},
 author = {Alkhalifa, Rabab and
Yoong, Theodore and
Kochkina, Elena and
Zubiaga, Arkaitz and
Liakata, Maria},
 crossref = {clef2020-workingnotes},
 title = {{QMUL-SDS} at {CheckThat!} 2020: {D}etermining {COVID-19} Tweet 
Check-Worthiness Using an Enhanced {CT-BERT} with Numeric Expressions}
}

@inproceedings{clef-checkthat-ar:2020,
 author = {Hasanain, Maram and    
Haouari, Fatima and
Suwaileh, Reem and
Ali, {Zien Sheikh} and
Hamdan, Bayan and 
Elsayed, Tamer and
Barr\'{o}n-Cede{\~n}o, Alberto and
{Da San Martino}, Giovanni and
Nakov, Preslav},
 crossref = {clef2020-workingnotes},
 title = {Overview of {CheckThat!} 2020 {A}rabic: Automatic Identification and Verification of Claims in Social Media}
}

@inproceedings{clef-checkthat-Bouziane:2020,
 abstract = {As part of the CheckThat! 2020 Task 2, we investigated sentence similarity using transformer models. In Task 2, the goal was to effectively rank claims based on their relevancy compared to an input tweet. While setting our baseline on sentence similarity for Fact-Checking, we gathered insights we felt compelled to share in this paper. We learned how multimodal data utilization could foster significant uplifts in model performance. We also gained knowledge on which hybrid training and strong sampling worked best for Fact-Checking applications, and wanted to share our interpretation of the results we got. Finally, we want to explain our recommendations on data augmentations. All of the above allowed us to set our baseline in Fact-Checking in the CLEF Checkthat! 2020 Task 2 competition.},
 author = {Bouziane, Mostafa and
Perrin, Hugo and
Cluzeau, Aur\`elien and
Mardas, Julien and
Sadeq, Amine},
 crossref = {clef2020-workingnotes},
 title = {{Buster.AI} at {CheckThat!} 2020: {I}nsights and recommendations to improve Fact-Checking.}
}

@inproceedings{clef-checkthat-cheema:2020,
 abstract = {In this paper, we focus on solving two problems in the fact-check ecosystem, claim check-worthiness prediction and claim retrieval. For the first problem, we explore the fusion of syntactic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, whether it includes a claim or not. We use Part-of-speech (POS) tags, named entities, and dependency relations as syntactic features and a combination of hidden layers in BERT to compute tweet embedding, and concatenate them to get a large feature embedding. We then reduce the feature size by applying PCA and train an SVM classifier for binary classification of tweets. We conduct a detailed analysis of syntactic feature combinations, word2vec embeddings and BERT hidden layer combinations to find the best performing model. For the second problem, we explore the pre-trained embeddings from a Siamese network transformer model (sentence transformers) specifically trained for semantic textual similarity and perform KD-search to retrieve claims. We further fine-tune these models using triplets with a triplet loss and conduct detailed experiments with different backbone networks.},
 author = {Cheema, Gullal S. and
Hakimov, Sherzod and
Ewerth, Ralph},
 crossref = {clef2020-workingnotes},
 title = {Check\_square at {CheckThat!} 2020: {C}laim Detection in Social Media via Fusion of Transformer and Syntactic Features}
}

@inproceedings{clef-checkthat-en:2020,
 author = {Shaar, Shaden and
Nikolov, Alex and
Babulkov, Nikolay and
Alam, Firoj and  
Barr\'{o}n-Cede{\~n}o, Alberto and
Elsayed, Tamer and
Hasanain, Maram and    
Suwaileh, Reem and
Haouari, Fatima and
{Da San Martino}, Giovanni and
Nakov, Preslav},
 crossref = {clef2020-workingnotes},
 title = {Overview of {CheckThat!} 2020 {E}nglish: Automatic Identification and Verification of Claims in Social Media}
}

@inproceedings{clef-checkthat-Hasanain:2020,
 abstract = {In this work we use Multilingual bERT pre-trained language model for ranking tweets by check-worthiness.},
 author = {Hasanain, Maram and
Elsayed, Tamer},
 crossref = {clef2020-workingnotes},
 title = {big{IR} at {CheckThat!} 2020: {M}ultilingual {BERT} for Ranking {A}rabic Tweets by Check-worthiness}
}

@inproceedings{clef-checkthat-Hussein:2020,
 abstract = {In recent years, online social networks like Twitter, Facebook, Instagram, and others have revolutionized interpersonal communication and it becomes an important platform to share information about current events. Consequently, the research on the informativeness of posts is becoming more important than ever before. In this paper, we present our approach to analyze the informativeness of Arabic information on Twitter. We used a hybrid system of convolutional neural networks and long-short term recurrent neural network models. To train the classification model, in addition to the dataset of 1500 tweets provided from CLEF 2020, we annotated for a data set of 5000 Arabic tweets -corresponding to 4 high impact news events of 2020 around the world. Results show that we can extract the informativeness of tweets with high effectiveness.},
 author = {Hussein, Ahmad and
Hussein, Abdulkarim and
Ghneim, Nada and
Joukhadar, Ammar},
 crossref = {clef2020-workingnotes},
 title = {{DamascusTeam} at {CheckThat!} 2020: {C}heck Worthiness on {T}witter with Hybrid {CNN} and {RNN} Models}
}

@inproceedings{clef-checkthat-Kartal:2020,
 abstract = {Today’s one of the most popular information resource is the social media and any information as well as misinformation can reach out to thousands of users in a short time. Therefore, automated systems that can prioritize claims based on their check-worthiness have a big role to prevent spreading of misinformation. In this paper, we propose three different models for CLEF Check That! Lab. In all our models, we use logistic regression. The first one uses BERT and word embeddings together. The second model is a hybrid combination of BERT and the first model such that we use only fine-tuned BERT for the first 15 claims. For the others, we use the first model. The last model is a modification of the first model with additional features which are pos tags, domain-specific controversial topics and a handcrafted word list. For the Arabic task, we use AraBert as our Bert model.  We use the first and second models for Task 1 Arabic and all models for Task 1 English and Task 5.
In the official evaluation of primary submissions, our primary models a) ranked 3rd in Task 1 Arabic based on P@30 and shared the 1st rank with another group on P@5,  b) ranked 4th in Task 1 English based on MAP and shared the 1st rank with 5 other groups on RR, P@1, P@3 and P@5, and c) ranked 3rd in Task 5 based on MAP.},
 author = {Kartal, Yavuz Selim and
Kutlu, Mucahid},
 crossref = {clef2020-workingnotes},
 title = {{TOBB ETU at CheckThat!} 2020: Prioritizing {E}nglish and {A}rabic Claims Based on Check-Worthiness}
}

@inproceedings{clef-checkthat-Martinez-Rico:2020,
 abstract = {Check-Worthiness and Claim Retrieval are two of the first tasks to 
be performed on the Fake News detection pipeline. In this article we present our 
approach to these tasks presented in the 2020 edition of the CheckThat! Labs. In 
the Task 1, Tweet Check-Worthiness English, we propose a Bi-LSTM model with 
Glove Twitter embeddings where the number of inputs has been increased with a 
graph generated from the additional information provided for each tweet. In Task 
1 Arabic we have followed a similar approach but using a FFNN model with Arabic 
embeddings. For the task 5, Debate Check-Worthiness, we propose a naive Bi-LSTM 
model with Glove embeddings. Finally our aproach to the Task 2, Claim Retrieval, 
is based in a FFNN model with features such as cosine similarity over USE 
embeddings of tweet and claim, and other linguistic features extracted from both 
elements.},
 author = {Martinez-Rico, {Juan R.} and
Araujo, Lourdes and
Martinez-Romo, Juan},
 crossref = {clef2020-workingnotes},
 title = {{NLP\&IR@UNED at CheckThat!} 2020: {A} Preliminary Approach for 
Check-Worthiness and Claim Retrieval Tasks using Neural Networks and Graphs}
}

@inproceedings{clef-checkthat-McDonald:2020,
 abstract = {The spread of misinformation online has been gathering pace in  
recent years, and whilst claim verification is an active area of research, the 
COVID-19 pandemic has presented a unique challenge due to the large amount of 
inaccurate information being shared on social media platforms. This paper 
describes our entries to Tasks 1 and 2 of the CLEF 2020 CheckThat! lab, which 
focus on the problems of determining check-worthiness and verification of claims 
found in tweets related to COVID-19. Our best performing approach for Task 1 
employed TF-IDF term weightings and a Random Forest model, ranking 17th out of 
27 systems. For Task 2, our best system involved the use of TF-IDF term 
weightings with a BM25 similarity score and a Support Vector Classifier scoring 
model, ranking 13th out of 22 systems.},
 author = {McDonald, Thomas and
Dong, ZiQing and
Zhang, Yingji and
Hampson, Rebekah and
Young, James and
Cao, Qianyu and 
Leidner, Jochen and
Stevenson, Mark},
 crossref = {clef2020-workingnotes},
 title = {The {U}niversity of {S}heffield at {CheckThat!} 2020: {C}laim 
Identification and Verification on {T}witter}
}

@inproceedings{clef-checkthat-Nikolov:2020,
 author = {Nikolov, Alex and {Da San Martino}, Giovanni and Koychev, Ivan and Nakov, Preslav},
 crossref = {clef2020-workingnotes},
 title = {{Team\_Alex at CheckThat!} 2020: Identifying check-worthy tweets with transformer models}
}

@inproceedings{clef-checkthat-Passaro:2020,
 abstract = {This paper describes a Fact Checking system based on a combination 
of Information Extraction and Deep Learning strategies to associate a tweet 
with the corresponding claim. The system has been built starting from a 
pre-trained Sentence-BERT model on which two cascade fine-tuning steps have been 
applied. The first step is a sentence similarity task aimed at predicting the 
cosine similarity between a tweet and a claim. Sentence-BERT was used to fine 
tune the model on this task by building a training set in which positive 
examples, consisting of gold tweet-claim pairs, were associated with a maximum 
cosine similarity. Negative examples were instead randomly selected from a list 
of candidate pairs with a non empty overlap in terms of metadata (named entities 
and keywords). Such examples were associated with the original cosine similarity 
between the tweet and claim, lowered by applying a penalty function. The second 
step consists in a binary classification task aimed at deciding whether a 
tweet-claim pair is a correct match or not. The model fine-tuned in the first 
step was enriched with a classifier head on top. Positive and negative examples 
were selected as above to construct the training set. The final ranking produced 
by the system is the predicted probability of the pair labelled as correct. 
Overall, the system reached a 0.91 MAP@5 on the test set.},
 author = {Passaro, {Lucia C.} and
Bondielli, Alessandro and
Lenci, Alessandro and
Marcelloni, Francesco},
 crossref = {clef2020-workingnotes},
 title = {{UNIPI-NLE} at {CheckThat!} 2020: {A}pproaching Fact Checking from a  
Sentence Similarity Perspective Through the Lens of Transformers}
}

@inproceedings{clef-checkthat-T1:2018,
 author = {Atanasova, Pepa and
Marquez, Lluis and
Barr\'{o}n-Cede{\~n}o, Alberto and
Elsayed, Tamer and
Suwaileh, Reem and 
Zaghouani, Wajdi and 
Kyuchukov, Spas and
Da San Martino, Giovanni and
Nakov, Preslav},
 crossref = {clef2018-workingnotes},
 title = {Overview of the {CLEF-2018 CheckThat!} Lab on Automatic Identification and Verification of Political Claims. {T}ask 1: Check-Worthiness}
}

@inproceedings{clef-checkthat-T1:2019,
 address = {Lugano, Switzerland},
 author = {Pepa Atanasova and Preslav Nakov and Georgi Karadzhov and Mitra Mohtarami and Giovanni Da San Martino},
 booktitle = {CLEF 2019 Working Notes},
 noeditor = {Cappellato, L. and Ferro, N. and Losada, D.E. and M\"uller, H. },
 noseries = {{CEUR} Workshop Proceedings},
 publisher = {CEUR-WS.org},
 title = {Overview of the {CLEF-2019 CheckThat! Lab on Automatic Identification and Verification of Claims. Task 1: Check-Worthiness}},
 year = {2019}
}

@inproceedings{clef-checkthat-T2:2018,
 author = {Barr\'{o}n-Cede{\~n}o, Alberto and
Elsayed, Tamer and
Suwaileh, Reem and 
Marquez, Lluis and
Atanasova, Pepa and
Zaghouani, Wajdi and 
Kyuchukov, Spas and        
Da San Martino, Giovanni and
Nakov, Preslav},
 crossref = {clef2018-workingnotes},
 title = {Overview of the {CLEF-2018 CheckThat!} Lab on Automatic Identification and Verification of Political Claims. {T}ask 2: Factuality}
}

@inproceedings{clef-checkthat-T2:2019,
 address = {Lugano, Switzerland},
 author = {Hasanain, Maram and Suwaileh, Reem and Elsayed, Tamer and Barr\'{o}n-Cede{\~n}o, Alberto and Nakov, Preslav},
 booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
 editor = {Cappellato, L. and Ferro, N. and Losada, D.E. and M\"uller, H. },
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Overview of the {CLEF-2019 CheckThat! Lab on Automatic Identification and Verification of Claims. Task 2: Evidence and Factuality}},
 year = {2019}
}

@inproceedings{clef-checkthat-Thuma:2020,
 abstract = {In this paper, we explore three different ad hoc retrieval approaches to rank verified claims, 
so that those that verify the input claim are ranked on top. In particular, we deploy DPH Divergence from Randomness (DFR) term weighting model to rank the verified claims.
In addition, we deploy the Sequential Dependence (SD) variant of the Markov Random Fields (MRF) for term dependence to re-rank documents (verified claims) that have query terms (input claim) in close proximity.
Moreover, we deploy LambdaMART, which is a learning to rank algorithm that use machine learning techniques to learn an appropriate combination of features into an effective 
ranking model.},
 author = {Thuma, Edwin and
Motlogelwa, Nkwebi Peace and
Leburu-Dingalo, Tebo and
Mudongo, Monkgogi},
 crossref = {clef2020-workingnotes},
 title = {{UB\_ET} at {CheckThat!} 2020: {E}xploring Ad hoc Retrieval Approaches in Verified Claims Retrieval}
}

@inproceedings{clef-checkthat-Touahri:2020,
 author = {Touahri, Ibtissam and
Mazroui, Azzeddine},
 crossref = {clef2020-workingnotes},
 title = {{EvolutionTeam} at {CheckThat!} 2020: {I}ntegration of linguistic and sentimental features in a fake news detection approach}
}

@inproceedings{clef-checkthat-williams:2020,
 abstract = {The Accenture team will describe experiments in using transformer fine-tuning to automatically fact-check tweets in English and Arabic.
We fine-tuned the English training data with the RoBERTa base model which was created and open-sourced by Liu et al \cite{roberta}. The base-roberta model was pretrained on 160GB of text extracted from BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. To help prevent overfitting, we added an extra mean pooling and dropout layer to the model.
We fine-tuned the Arabic training data with 3 different pre-trained Arabic BERT models- two of which were not trained on stemmed tokens. We used two BERT bases which were pretrained and made open source by Wissam Antoun, Fady Baly, and Hazem Hajj at the American University of Beiruit \cite{aubmindlabs}. The Aubmindlabs Arabert was pretrained on Arabic Articles manually scraped by researchers along with two open source corpora: (1) the 1.5 billion word Arabic Corpus, which contains more than 5 million news articles from ten major news sources. and (2) OSIAN: the Open Source International Arabic News Corpus, which includes more than 3.5 million articles from 31  news sources \cite{aubmindlabs}
In order to improve label balance in the Arabic corpus, and to improve lexical diversity, we used machine translation as an external data source to upsample the positive class of the Arabic training data.  We took the Arabic text, and used Amazon Translate to translate this into English and then back to Arabic.  These translated tweets served as additional training data.
While metadata was provided for the Arabic and English tweets, this approach utilized only the language content.},
 author = {Williams, Evan and
Rodrigues, Paul and
Novak, Valerie},
 crossref = {clef2020-workingnotes},
 title = {Accenture at {CheckThat! 2020: I}f you say so: Post-hoc fact-checking of claims using transformer-based models}
}

@inproceedings{clef-checkthat:2019,
 author = {Elsayed, Tamer and
Nakov, Preslav and
Barr\'{o}n-Cede{\~n}o, Alberto and
Hasanain, Maram and
Suwaileh, Reem and
{Da San Martino}, Giovanni and 
Atanasova, Pepa},
 booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
 pages = {301--321},
 publisher = {Springer},
 series = {LNCS},
 title = {{Overview of the CLEF-2019 CheckThat!}: Automatic Identification and Verification of Claims}
}

@proceedings{clef2018-workingnotes,
 editor = {Cappellato, Linda  and
Ferro, Nicola and
Nie, Jian-Yun and
Soulier, Laure},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Working Notes of CLEF 2018--Conference and Labs of the Evaluation Forum}
}

@inproceedings{clef2018checkthat,
 address = {Avignon, France},
 author = {Nakov, Preslav and 
Barr\'{o}n-Cede{\~n}o, Alberto and 
Elsayed, Tamer and 
Suwaileh, Reem and 
M\`{a}rquez, Llu\'{i}s and 
Zaghouani, Wajdi and 
Gencheva, Pepa and 
Kyuchukov, Spas and 
{Da San Martino}, Giovanni},
 booktitle = {Working Notes of {CLEF} 2018 -- Conference and Labs of the Evaluation Forum},
 series = {CLEF~'18},
 title = {Overview of the {CLEF}-2018 Lab on Automatic Identification and Verification of Claims in Political Debates}
}

@inproceedings{clef2018checkthat:overall,
 address = {Avignon, France},
 author = {Nakov, Preslav  and  Barr\'{o}n-Cede\~{n}o, Alberto and Elsayed, Tamer and Suwaileh, Reem and M\`{a}rquez, Llu\'{i}s and Zaghouani, Wajdi and Atanasova, Pepa and Kyuchukov, Spas and Da San Martino, Giovanni},
 booktitle = {Proc. of the Ninth International Conference of the CLEF},
 noeditor = {Patrice Bellot, Chiraz Trabelsi, Josiane Mothe, Fionn Murtagh, Jian Yun Nie, Laure Soulier, Eric Sanjuan, Linda Cappellato, Nicola Ferro},
 pages = {372--387},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 title = {Overview of the {CLEF-2018 CheckThat!} Lab on Automatic Identification and Verification of Political Claims},
 year = {2018}
}

@inproceedings{clef2018checkthat:task1,
 address = {Avignon, France},
 author = {Atanasova, Pepa and  M\`{a}rquez, Llu\'{i}s  and Barr\'{o}n-Cede\~{n}o, Alberto  and Elsayed, Tamer and Suwaileh, Reem and Zaghouani, Wajdi and Kyuchukov, Spas and Da San Martino, Giovanni and Nakov, Preslav},
 booktitle = {CLEF 2018 Working Notes. Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
 noeditor = {Cappellato, Linda and Ferro, Nicola and Nie, Jian-Yun and Soulier, Laure},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Overview of the {CLEF-2018 CheckThat!} Lab on Automatic Identification and Verification of Political Claims, Task 1: Check-Worthiness},
 year = {2018}
}

@inproceedings{clef2018checkthat:task2,
 address = {Avignon, France},
 author = {Barr\'{o}n-Cede\~{n}o, Alberto and Elsayed, Tamer and Suwaileh, Reem and M\`{a}rquez, Llu\'{i}s  and Atanasova, Pepa and Zaghouani, Wajdi and Kyuchukov, Spas and Da San Martino, Giovanni and Nakov, Preslav},
 booktitle = {CLEF 2018 Working Notes. Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
 noeditor = {Cappellato, Linda and Ferro, Nicola and Nie, Jian-Yun and Soulier, Laure},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Overview of the {CLEF-2018 CheckThat!} Lab on Automatic Identification and Verification of Political Claims, Task 2: Factuality},
 year = {2018}
}

@proceedings{clef2020-workingnotes,
 editor = {Cappellato, Linda 
and Eickhoff, Carsten 
and Ferro, Nicola and
Névéol, Aurélie},
 issn = {1613-0073},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {CLEF 2020 Working Notes}
}

@proceedings{CLEF_LNCS:20,
 editor = {Arampatzis, Avi and
Kanoulas, Evangelos and 
Tsikrika, Theodora and 
Vrochidis, Stefanos and 
Joho, Hideo and 
Lioma, Christina and 
Eickhoff, Carsten and
Névéol, Aurélie and 
Cappellato, Linda and 
Ferro, Nicola },
 publisher = {Springer},
 series = {LNCS (12260)},
 title = {Experimental IR Meets Multilinguality, Multimodality, and Interaction
Proceedings of the Eleventh International Conference of the CLEF Association 
(CLEF 2020)}
}

@proceedings{ClefCeur:2019,
 editor = {Cappellato, Linda and
Ferro, Nicola and
Losada, {David E.} and
M{\"u}ller, Henning},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Working Notes of {CLEF} 2019 Conference and Labs of the Evaluation Forum},
 year = {2019}
}

@proceedings{ClefCeur:2019,
 editor = {Cappellato, Linda and
Ferro, Nicola and
Losada, {David E.} and
M{\"u}ller, Henning},
 publisher = {CEUR-WS.org},
 series = {{CEUR} Workshop Proceedings},
 title = {Working Notes of {CLEF} 2019 Conference and Labs of the Evaluation Forum},
 year = {2019}
}

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 editor = {Ravi Kumar and
James Caverlee and
Hanghang Tong},
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 publisher = {{IEEE}},
 timestamp = {Tue, 23 May 2017 01:00:00 +0200},
 title = {Stop Clickbait: Detecting and preventing clickbaits in online news
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 url = {https://doi.org/10.1109/ASONAM.2016.7752207},
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@misc{codedisinfo2021,
 author = {DIGI, Digital Industry Group Inc},
 date-added = {2021-02-24 10:33:08 +0000},
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 url = {https://www.aclweb.org/anthology/P08-1019},
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@inproceedings{Duchi11,
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Elad Hazan and
Yoram Singer},
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 booktitle = {{COLT} 2010 - The 23rd Conference on Learning Theory, Haifa, Israel,
June 27-29, 2010},
 editor = {Adam Tauman Kalai and
Mehryar Mohri},
 pages = {257--269},
 publisher = {Omnipress},
 timestamp = {Tue, 19 Feb 2013 00:00:00 +0100},
 title = {Adaptive Subgradient Methods for Online Learning and Stochastic Optimization},
 url = {http://colt2010.haifa.il.ibm.com/papers/COLT2010proceedings.pdf\#page=265},
 year = {2010}
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@inproceedings{dungs-EtAl:2018:C18-1,
 address = {Santa Fe, New Mexico, USA},
 author = {Dungs, Sebastian  and
Aker, Ahmet  and
Fuhr, Norbert  and
Bontcheva, Kalina},
 booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
 pages = {3360--3370},
 publisher = {Association for Computational Linguistics},
 title = {Can Rumour Stance Alone Predict Veracity?},
 url = {https://www.aclweb.org/anthology/C18-1284},
 year = {2018}
}

@inproceedings{duong2015,
 address = {Beijing, China},
 author = {Duong, Long  and
Cohn, Trevor  and
Bird, Steven  and
Cook, Paul},
 booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
 doi = {10.3115/v1/P15-2139},
 pages = {845--850},
 publisher = {Association for Computational Linguistics},
 title = {Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser},
 url = {https://www.aclweb.org/anthology/P15-2139},
 year = {2015}
}

@article{durodolu2020fake,
 author = {Durodolu, Oluwole Olumide and Ibenne, Samuel Kelechukwu},
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 year = {2020}
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@inproceedings{Echihabi:2003:NAQ:1075096.1075099,
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 author = {Echihabi, Abdessamad  and
Marcu, Daniel},
 booktitle = {Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics},
 doi = {10.3115/1075096.1075099},
 pages = {16--23},
 publisher = {Association for Computational Linguistics},
 title = {A Noisy-Channel Approach to Question Answering},
 url = {https://www.aclweb.org/anthology/P03-1003},
 year = {2003}
}

@article{Edwards1997RepresentativeFA,
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 year = {1997}
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@inproceedings{EMNLP19DaSanMartino,
 address = {Hong Kong, China},
 author = {Da San Martino, Giovanni  and
Yu, Seunghak  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Petrov, Rostislav  and
Nakov, Preslav},
 booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
 doi = {10.18653/v1/D19-1565},
 pages = {5636--5646},
 publisher = {Association for Computational Linguistics},
 title = {Fine-Grained Analysis of Propaganda in News Article},
 url = {https://www.aclweb.org/anthology/D19-1565},
 series = {EMNLP-IJCNLP~'19},
 year = {2019}
}

@inproceedings{EMNLP2019:fauxtography,
 address = {Hong Kong, China},
 author = {Zlatkova, Dimitrina  and
Nakov, Preslav  and
Koychev, Ivan},
 booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
 doi = {10.18653/v1/D19-1216},
 pages = {2099--2108},
 publisher = {Association for Computational Linguistics},
 title = {Fact-Checking Meets Fauxtography: Verifying Claims About Images},
 url = {https://www.aclweb.org/anthology/D19-1216},
 year = {2019}
}

@inproceedings{ennals2010disputed,
 address = {New York, NY, USA},
 author = {Ennals, Rob and Byler, Dan and Agosta, John Mark and Rosario, Barbara},
 booktitle = {Proceedings of the 4th Workshop on Information Credibility},
 noorganization = {ACM},
 pages = {67--74},
 series = {WICOW~'10},
 title = {What is disputed on the web?},
 year = {2010}
}

@inproceedings{ennals2010highlighting,
 author = {Rob Ennals and
Beth Trushkowsky and
John Mark Agosta},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/www/EnnalsTA10.bib},
 booktitle = {Proceedings of the 19th International Conference on World Wide Web,
{WWW} 2010, Raleigh, North Carolina, USA, April 26-30, 2010},
 doi = {10.1145/1772690.1772726},
 editor = {Michael Rappa and
Paul Jones and
Juliana Freire and
Soumen Chakrabarti},
 pages = {341--350},
 publisher = {{ACM}},
 timestamp = {Tue, 06 Nov 2018 00:00:00 +0100},
 title = {Highlighting disputed claims on the web},
 url = {https://doi.org/10.1145/1772690.1772726},
 year = {2010}
}

@article{pramanick2021momenta,
  title={MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets},
  author={Pramanick, Shraman and Sharma, Shivam and Dimitrov, Dimitar and Akhtar, Md Shad and Nakov, Preslav and Chakraborty, Tanmoy},
  journal={arXiv preprint arXiv:2109.05184},
  year={2021}
}

@inproceedings{pramanick-acl,
    title = "Detecting Harmful Memes and Their Targets",
    author = "Pramanick, Shraman  and
      Dimitrov, Dimitar  and
      Mukherjee, Rituparna  and
      Sharma, Shivam  and
      Akhtar, Md. Shad  and
      Nakov, Preslav  and
      Chakraborty, Tanmoy",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.246",
    doi = "10.18653/v1/2021.findings-acl.246",
    pages = "2783--2796",
}

@inproceedings{Eyben:2013:RDO:2502081.2502224,
 acmid = {2502224},
 address = {Barcelona, Spain},
 author = {Eyben, Florian and Weninger, Felix and Gross, Florian and Schuller, Bj\"{o}rn},
 booktitle = {Proceedings of the International Conference on Multimedia},
 isbn = {978-1-4503-2404-5},
 keywords = {acoustic features, affect recognition, affective computing, audio features, computational paralinguistics, feature extraction, machine learning, multimedia analysis, openSMILE, video features, visual features},
 noaddress = {New York, NY, USA},
 nodoi = {10.1145/2502081.2502224},
 nopublisher = {ACM},
 nourl = {http://NOdoi.acm.org/10.1145/2502081.2502224},
 numpages = {4},
 pages = {835--838},
 series = {MM '13},
 title = {Recent Developments in {openSMILE}, the {M}unich Open-source Multimedia Feature Extractor},
 year = {2013}
}

@misc{fakeddit,
 author = {Nakamura, Kai and Levy, Sharon and Wang, William Yang},
 howpublished = {\url{https://github.com/entitize/fakeddit}},
 note = {2020},
 title = {Fakeddit dataset}
}

@misc{fakenewnetdata,
 author = {Shu, Kai and Mahudeswaran, Deepak and Wang, Suhang and Lee, Dongwon and Liu, Huan},
 howpublished = {\url{https://github.com/KaiDMML/FakeNewsNet}},
 note = {2018},
 title = {FakeNewNet dataset}
}

@manual{Fear05,
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 note = {\url{http://www.ctan.org/pkg/booktabs}},
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@article{feng2015applying,
 author = {Feng, Minwei and Xiang, Bing and Glass, Michael R and Wang, Lidan and Zhou, Bowen},
 journal = {arXiv:1508.01585},
 title = {Applying deep learning to answer selection: A study and an open task},
 year = {2015}
}

@article{ferrara2020covid,
 author = {Ferrara, Emilio},
 journal = {arXiv preprint arXiv:2004.09531},
 title = {\# covid-19 on twitter: Bots, conspiracies, and social media activism},
 year = {2020}
}

@inproceedings{ferreira-vlachos:2016:N16-1,
 address = {San Diego, California},
 author = {Ferreira, William  and
Vlachos, Andreas},
 booktitle = {Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
 doi = {10.18653/v1/N16-1138},
 pages = {1163--1168},
 publisher = {Association for Computational Linguistics},
 title = {{E}mergent: a novel data-set for stance classification},
 url = {https://www.aclweb.org/anthology/N16-1138},
 year = {2016}
}

@inproceedings{ferreira2016emergent,
 address = {San Diego, California},
 author = {Ferreira, William  and
Vlachos, Andreas},
 booktitle = {Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
 doi = {10.18653/v1/N16-1138},
 pages = {1163--1168},
 publisher = {Association for Computational Linguistics},
 title = {{E}mergent: a novel data-set for stance classification},
 url = {https://www.aclweb.org/anthology/N16-1138},
 year = {2016}
}

@misc{figa,
 author = {the POKE},
 howpublished = {\url https://politics.theonion.com/trump-admits-18-new-states-to-increase-competition-for-1842708962},
 note = {2020},
 title = {The Queen’s dress made the perfect green screen and people hilariously exploited it}
}

@misc{figb,
 author = {The ONION},
 howpublished = {\url https://www.thepoke.co.uk/2020/04/08/queens-dress-perfect-green-screen-hilariously-exploited/},
 note = {2020},
 title = {Trump Admits 18 New States To Increase Competition For Medical Supplies}
}

@misc{figc,
 author = {The ONION},
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@article{finberg2002digital,
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@manual{Fiorio15,
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 author = {Cristophe Fiorio},
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@article{fisher2013syrian,
 author = {Fisher, Max},
 journal = {Washington Post},
 title = {Syrian hackers claim AP hack that tipped stock market by \$136 billion. Is it terrorism},
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@article{fleiss1971mns,
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 journal = {Psychological Bulletin},
 keywords = {inter-rater measure reliability},
 pages = {378--382},
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@book{fleiss2013statistical,
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 title = {Statistical methods for rates and proportions},
 year = {2013}
}



@article{fortuna2021well,
 author = {Fortuna, Paula and Soler-Company, Juan and Wanner, Leo},
 journal = {Information Processing \& Management},
 number = {3},
 pages = {102524},
 publisher = {Elsevier},
 title = {How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?},
 volume = {58},
 year = {2021}
}

@inproceedings{Foster:2009:SME,
 address = {Athens, Greece},
 author = {Foster, George  and
Kuhn, Roland},
 booktitle = {Proceedings of the Fourth Workshop on Statistical Machine Translation},
 pages = {242--249},
 publisher = {Association for Computational Linguistics},
 title = {Stabilizing Minimum Error Rate Training},
 url = {https://www.aclweb.org/anthology/W09-0439},
 year = {2009}
}

@article{Friedman:01,
 abstract = {Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent "boosting" paradigm is developed for additive expansions based on any fitting criterion. Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such "TreeBoost" models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.},
 author = {Jerome H. Friedman},
 issn = {00905364},
 journal = {The Annals of Statistics},
 number = {5},
 pages = {1189--1232},
 publisher = {Institute of Mathematical Statistics},
 title = {Greedy Function Approximation: A Gradient Boosting Machine},
 url = {http://www.jstor.org/stable/2699986},
 volume = {29},
 year = {2001}
}

@article{Fuhr:18,
 author = {Fuhr, Norbert and 
Nejdl, Wolfgang and 
Peters, Isabella and 
Stein, Benno and 
Giachanou, Anastasia and 
Grefenstette, Gregory and 
Gurevych, Iryna and 
Hanselowski, Andreas and 
Jarvelin, Kalervo and 
Jones, Rosie and 
Liu, YiquN and 
Mothe, Josiane},
 journal = {{ACM SIGIR Forum}},
 pages = {46--66},
 title = {{An Information Nutritional Label for Online Documents}},
 volume = {51}
}

@article{fullfact,
 author = {Babakar, Mevan and Moy, Will},
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 title = {The State of Automated Factchecking},
 year = {2016}
}

@article{gallotti2020assessing,
 author = {Gallotti, Riccardo and Valle, Francesco and Castaldo, Nicola and Sacco, Pierluigi and De Domenico, Manlio},
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 title = {Assessing the risks of" infodemics" in response to COVID-19 epidemics},
 year = {2020}
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@inproceedings{gangireddy2020unsupervised,
 author = {Gangireddy, Siva Charan Reddy and Long, Cheng and Chakraborty, Tanmoy},
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 pages = {75--83},
 title = {Unsupervised Fake News Detection: A Graph-based Approach},
 year = {2020}
}

@article{garimella2020images,
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 title = {{Images and misinformation in political groups: Evidence from WhatsApp in India}},
 year = {2020}
}

@article{gebru2018datasheets,
 author = {Gebru, Timnit and Morgenstern, Jamie and Vecchione, Briana and Vaughan, Jennifer Wortman and Wallach, Hanna and Daum{\'e} III, Hal and Crawford, Kate},
 journal = {arXiv preprint arXiv:1803.09010},
 title = {Datasheets for datasets},
 year = {2018}
}

@inproceedings{gencheva-EtAl:2017:RANLP,
 address = {Varna, Bulgaria},
 author = {Gencheva, Pepa  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_037},
 pages = {267--276},
 publisher = {INCOMA Ltd.},
 title = {A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates},
 url = {https://doi.org/10.26615/978-954-452-049-6_037},
 year = {2017}
}

@inproceedings{gencheva2017context,
 address = {Varna, Bulgaria},
 author = {Gencheva, Pepa  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_037},
 pages = {267--276},
 publisher = {INCOMA Ltd.},
 title = {A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates},
 url = {https://doi.org/10.26615/978-954-452-049-6_037},
 year = {2017}
}

@inproceedings{ghaeini2018dr,
 address = {New Orleans, Louisiana},
 author = {Ghaeini, Reza  and
Hasan, Sadid A.  and
Datla, Vivek  and
Liu, Joey  and
Lee, Kathy  and
Qadir, Ashequl  and
Ling, Yuan  and
Prakash, Aaditya  and
Fern, Xiaoli  and
Farri, Oladimeji},
 booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
 doi = {10.18653/v1/N18-1132},
 pages = {1460--1469},
 publisher = {Association for Computational Linguistics},
 title = {{DR}-{B}i{LSTM}: Dependent Reading Bidirectional {LSTM} for Natural Language Inference},
 url = {https://www.aclweb.org/anthology/N18-1132},
 year = {2018}
}

@article{ghanem2020emotional,
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 author = {Ghanem, Bilal and Rosso, Paolo and Rangel, Francisco},
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}

@inproceedings{Ghanem:2018,
 address = {Avignon, France},
 author = {Ghanem, Bilal and Montes-y-G\'{o}mez, Manuel and Rangel, Francisco and Rosso, Paolo},
 booktitle = {CLEF 2018 Working Notes},
 noeditor = {Cappellato, Linda and Ferro, Nicola and Nie, Jian-Yun and Soulier, Laure},
 nopublisher = {CEUR-WS.org},
 series = {CLEF~'2018},
 title = {{UPV-INAOE} - {Check That}: Preliminary Approach for Checking Worthiness of Claims},
 year = {2018}
}

@article{giannakopoulos2009study,
 author = {Giannakopoulos, Theodoros},
 journal = {University of Athens},
 title = {Study and application of acoustic information for the detection of harmful content, and fusion with visual information},
 year = {2009}
}

@inproceedings{gilbert2014vader,
 address = {Ann Arbor, MI, USA},
 author = {Hutto, Clayton and Gilbert, Eric},
 booktitle = {Proceedings of the 8th International Conference on Weblogs and Social Media},
 series = {ICWSM~'14},
 title = {{VADER}: A parsimonious rule-based model for sentiment analysis of social media text},
 year = {2014}
}

@misc{gitEchen,
 author = {{Echen}},
 howpublished = {\url{https://github.com/echen102/covid-19-TweetIDs}},
 note = {Online; accessed 2020},
 title = {{COVID-19-TweetIDs-GIT}}
}

@article{glenski2018propagation,
 author = {Glenski, Maria and Weninger, Tim and Volkova, Svitlana},
 journal = {IEEE Transactions on Computational Social Systems},
 number = {4},
 pages = {1071--1082},
 publisher = {IEEE},
 title = {Propagation from deceptive news sources who shares, how much, how evenly, and how quickly?},
 volume = {5},
 year = {2018}
}

@article{glenski2019multilingual,
 author = {Glenski, Maria and Ayton, Ellyn and Mendoza, Josh and Volkova, Svitlana},
 journal = {arXiv preprint arXiv:1909.05838},
 title = {Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms},
 year = {2019}
}

@article{Glenski2019MultilingualMD,
 author = {Glenski, Maria and Ayton, Ellyn and Mendoza, Josh and Volkova, Svitlana},
 journal = {arXiv:1909.05838},
 title = {Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms},
 year = {2019}
}

@article{gls:hypertrees,
 author = {Georg Gottlob and Nicola Leone and Francesco Scarcello},
 journal = {Journal of Computer and System Sciences},
 number = {3},
 pages = {579--627},
 title = {Hypertree Decompositions and Tractable Queries},
 volume = {64},
 year = {2002}
}

@article{go2009twitter,
 author = {Go, Alec and Bhayani, Richa and Huang, Lei},
 journal = {CS224N project report, Stanford},
 number = {12},
 pages = {2009},
 title = {Twitter sentiment classification using distant supervision},
 volume = {1},
 year = {2009}
}

@article{goebel2019deep,
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 journal = {Electronic Imaging},
 number = {5},
 pages = {530--1},
 publisher = {Society for Imaging Science and Technology},
 title = {Deep learning methods for event verification and image repurposing detection},
 volume = {2019},
 year = {2019}
}

@inproceedings{goel2012structure,
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 booktitle = {Proceedings of the 13th ACM conference on electronic commerce},
 pages = {623--638},
 title = {The structure of online diffusion networks},
 year = {2012}
}

@inproceedings{golbeck2018fake,
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 booktitle = {Proceedings of the 10th ACM Conference on Web Science},
 pages = {17--21},
 title = {Fake news vs satire: A dataset and analysis},
 year = {2018}
}

@article{goljan2010defending,
 author = {Goljan, Miroslav and Fridrich, Jessica and Chen, Mo},
 journal = {IEEE Transactions on Information Forensics and Security},
 number = {1},
 pages = {227--236},
 publisher = {IEEE},
 title = {Defending against fingerprint-copy attack in sensor-based camera identification},
 volume = {6},
 year = {2010}
}

@inproceedings{Gomez2020exploring,
 author = {Gomez, Raul and Gibert, Jaume and Gomez, Lluis and Karatzas, Dimosthenis},
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 pages = {1470--1478},
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 year = {2020}
}

@inproceedings{gorrell-etal-2019-semeval,
 address = {Vancouver, Canada},
 author = {Derczynski, Leon  and
Bontcheva, Kalina  and
Liakata, Maria  and
Procter, Rob  and
Wong Sak Hoi, Geraldine  and
Zubiaga, Arkaitz},
 booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)},
 doi = {10.18653/v1/S17-2006},
 pages = {69--76},
 publisher = {Association for Computational Linguistics},
 title = {{S}em{E}val-2017 Task 8: {R}umour{E}val: Determining rumour veracity and support for rumours},
 url = {https://www.aclweb.org/anthology/S17-2006},
 year = {2017}
}

@inproceedings{gorrell2019semeval,
 address = {Vancouver, Canada},
 author = {Derczynski, Leon  and
Bontcheva, Kalina  and
Liakata, Maria  and
Procter, Rob  and
Wong Sak Hoi, Geraldine  and
Zubiaga, Arkaitz},
 booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)},
 doi = {10.18653/v1/S17-2006},
 pages = {69--76},
 publisher = {Association for Computational Linguistics},
 title = {{S}em{E}val-2017 Task 8: {R}umour{E}val: Determining rumour veracity and support for rumours},
 url = {https://www.aclweb.org/anthology/S17-2006},
 year = {2017}
}

@article{gottlob:nonmon,
 author = {Georg Gottlob},
 journal = {Journal of Logic and Computation},
 number = {3},
 pages = {397--425},
 title = {Complexity results for nonmonotonic logics},
 volume = {2},
 year = {1992}
}

@article{graham2009liberals,
 author = {Graham, Jesse and Haidt, Jonathan and Nosek, Brian A},
 journal = {Journal of personality and social psychology},
 number = {5},
 pages = {1029},
 publisher = {US: American Psychological Association},
 title = {Liberals and conservatives rely on different sets of moral foundations.},
 volume = {96},
 year = {2009}
}

@article{Granados:11,
 author = {Granados, Ana and 
Cebrian, Manuel and 
Camacho, David and 
{de Borja Rodriguez}, Francisco},
 journal = {IEEE Transactions on Knowledge and Data Engineering},
 number = {7},
 pages = {1090--1102},
 title = {Reducing the Loss of Information through Annealing Text Distortion},
 volume = {23}
}

@inproceedings{granik2017fake,
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 title = {{VADER: A parsimonious rule-based model for sentiment analysis of social media text}},
 volume = {8},
 year = {2014}
}

@book{hyland1998hedging,
 author = {Hyland, Ken},
 publisher = {John Benjamins Publishing},
 title = {Hedging in scientific research articles},
 volume = {54},
 year = {1998}
}

@book{hyland2005metadiscourse,
 author = {Hyland, K.},
 isbn = {9781441155979},
 publisher = {Bloomsbury Publishing},
 series = {Continuum Discourse},
 title = {Metadiscourse: Exploring Interaction in Writing},
 year = {2005}
}

@article{info:doi/10.2196/19273,
 abstract = {Background: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective: In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods: We started this ongoing data collection on January 28, 2020, leveraging Twitter's streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter's search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results: Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60{\%} of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions: It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic---and undoubtedly more. },
 author = {Chen, Emily
and Lerman, Kristina
and Ferrara, Emilio},
 day = {29},
 issn = {2369-2960},
 journal = {JMIR Public Health Surveill},
 keywords = {COVID-19; SARS-CoV-2; social media; network analysis; computational social sciences},
 nodoi = {10.2196/19273},
 nourl = {http://publichealth.jmir.org/2020/2/e19273/},
 number = {2},
 pages = {e19273},
 title = {Tracking Social Media Discourse About the {COVID}-19 Pandemic: Development of a Public Coronavirus {T}witter Data Set},
 volume = {6},
 year = {2020}
}

@inproceedings{inproceedings-is2010-paralinguistic-challenge,
 address = {Makuhari, Japan},
 author = {Schuller, Bj{\"o}rn and Steidl, Stefan and Batliner, Anton and Burkhardt, Felix and Devillers, Laurence and A. Müller, Christian and Narayanan, Shrikanth},
 booktitle = {Interspeech},
 pages = {2794--2797},
 series = {INTERSPEECH~'10},
 title = {The {INTERSPEECH} 2010 Paralinguistic Challenge},
 year = {2010}
}

@inproceedings{inproceedings-is2011-speaker-state,
 address = {Florence, Italy},
 author = {Schuller, Bj{\"o}rn and Batliner, Anton and Steidl, Stefan and Schiel, Florian and Krajewski, Jarek},
 booktitle = {Proceedings of the 12th Annual Conference of the International Speech Communication Association},
 pages = {3201-3204},
 series = {INTERSPEECH~'11},
 title = {The {INTERSPEECH} 2011 Speaker State Challenge},
 year = {2011}
}

@inproceedings{inproceedings-is2012-speaker-trait,
 address = {Portland, OR, USA},
 author = {Schuller, Bj{\"o}rn and Steidl, Stefan and Batliner, Anton and Noeth, Elmar and Vinciarelli, Alessandro and Burkhardt, Felix and van Son, Rob and Weninger, Felix and Eyben, Florian and Bocklet, Tobias and Mohammadi, Gelareh and Weiss, Benjamin},
 booktitle = {Proceedings of the 13th Annual Conference of the International Speech Communication Association},
 pages = {254-257},
 series = {INTERSPEECH~'12},
 title = {The {INTERSPEECH} 2012 Speaker Trait Challenge},
 year = {2012}
}

@manual{instr-l,
 note = {\url{http://www.ctan.org/pkg/amslatex}},
 organization = {American Mathematical Society},
 title = {Instructions for Preparation of Papers and
Monographs, {AMS\LaTeX}}
}

@article{InternetResearchJournal:2018,
 author = {Mihaylov, Todor and Mihaylova, Tsvetomila and Nakov, Preslav and M\`{a}rquez, Llu\'{i}s and Georgiev, Georgi and Koychev, Ivan},
 journal = {Internet Research},
 title = {The Dark Side of News Community Forums: Opinion Manipulation Trolls},
 year = {2018}
}

@proceedings{INTERSPEECH18,
 booktitle = {{INTERSPEECH} 2019 -- 20\textsuperscript{th} Annual Conference of the International Speech Communication Association, September 15-19, Graz, Austria, Proceedings},
 date-modified = {2019-01-20 09:24:27 +0000},
 key = {INTERSPEECH},
 title = {{INTERSPEECH} 2019 -- 20\textsuperscript{th} Annual Conference of the International Speech Communication Association, September 15-19, Graz, Austria, Proceedings},
 year = {2019}
}



@inproceedings{Ishikawa:10,
 author = {Ishikawa, Daisuke and
Sakai, Tetsuya and
Kando, Noriko },
 crossref = {ntcir:8},
 pages = {421--432},
 title = {{Overview of the NTCIR-8 Community QA Pilot Task (Part I): The Test Collection and the Task}}
}

@article{Islam2020,
 author = {Islam, Md Rafiqul and Liu, Shaowu and Wang, Xianzhi and Xu, Guandong},
 journal = {SNAM},
 keywords = {Decision making,Deep learning,Misinformation detection,Neural network,Online social networks},
 nodoi = {10.1007/s13278-020-00696-x},
 noisbn = {0123456789},
 noissn = {18695469},
 nourl = {https://doi.org/10.1007/s13278-020-00696-x},
 number = {1},
 pages = {1--20},
 publisher = {Springer Vienna},
 title = {Deep learning for misinformation detection on online social networks: a survey and new perspectives},
 volume = {10},
 year = {2020}
}

@inproceedings{jaiswal2017multimedia,
 author = {Jaiswal, Ayush and Sabir, Ekraam and AbdAlmageed, Wael and Natarajan, Premkumar},
 booktitle = {Proceedings of the 25th ACM international conference on Multimedia},
 pages = {1465--1471},
 title = {Multimedia semantic integrity assessment using joint embedding of images and text},
 year = {2017}
}

@inproceedings{jaiswal2019aird,
 author = {Ayush Jaiswal and
Yue Wu and
Wael AbdAlmageed and
Iacopo Masi and
Premkumar Natarajan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cvpr/Jaiswal0AMN19.bib},
 booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2019, Long Beach, CA, USA, June 16-20, 2019},
 doi = {10.1109/CVPR.2019.01159},
 pages = {11330--11339},
 publisher = {Computer Vision Foundation / {IEEE}},
 timestamp = {Mon, 20 Jan 2020 00:00:00 +0100},
 title = {{AIRD:} Adversarial Learning Framework for Image Repurposing Detection},
 url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Jaiswal\_AIRD\_Adversarial\_Learning\_Framework\_for\_Image\_Repurposing\_Detection\_CVPR\_2019\_paper.html},
 year = {2019}
}

@book{jamieson2008echo,
 author = {Jamieson, Kathleen Hall and Cappella, Joseph N},
 publisher = {Oxford University Press},
 title = {Echo chamber: Rush Limbaugh and the conservative media establishment},
 year = {2008}
}

@article{jang2018computational,
 author = {Jang, S Mo and Geng, Tieming and Li, Jo-Yun Queenie and Xia, Ruofan and Huang, Chin-Tser and Kim, Hwalbin and Tang, Jijun},
 journal = {Computers in Human Behavior},
 pages = {103--113},
 publisher = {Elsevier},
 title = {A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis},
 volume = {84},
 year = {2018}
}

@article{jang2018harmful,
 author = {Jang, Seok-Woo and Lee, Sang-Hong},
 journal = {Journal of Sensors},
 publisher = {Hindawi},
 title = {Harmful content detection based on cascaded adaptive boosting},
 volume = {2018},
 year = {2018}
}

@article{jang2019fake,
 author = {Jang, Yonghun and Park, Chang-Hyeon and Seo, Yeong-Seok},
 journal = {Electronics},
 number = {12},
 pages = {1377},
 publisher = {Multidisciplinary Digital Publishing Institute},
 title = {Fake news analysis modeling using quote retweet},
 volume = {8},
 year = {2019}
}

@article{jarvelin2002cumulated,
 author = {J{\"a}rvelin, Kalervo and Kek{\"a}l{\"a}inen, Jaana},
 journal = {ACM Transactions on Information Systems (TOIS)},
 number = {4},
 pages = {422--446},
 publisher = {ACM},
 title = {Cumulated gain-based evaluation of IR techniques},
 volume = {20},
 year = {2002}
}

@inproceedings{jeon2005finding,
 author = {Jeon, Jiwoon and Croft, W Bruce and Lee, Joon Ho},
 booktitle = {Proceedings of the 14th ACM international conference on Information and knowledge management},
 organization = {ACM},
 pages = {84--90},
 title = {Finding similar questions in large question and answer archives},
 year = {2005}
}

@inproceedings{Jeon:2006:FPQ:1148170.1148212,
 acmid = {1148212},
 address = {New York, NY, USA},
 author = {Jeon, Jiwoon and Croft, W. Bruce and Lee, Joon Ho and Park, Soyeon},
 booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
 isbn = {1-59593-369-7},
 keywords = {document quality, information retrieval, language models, maximum entropy},
 location = {Seattle, Washington, USA},
 numpages = {8},
 pages = {228--235},
 publisher = {ACM},
 series = {SIGIR '06},
 title = {A Framework to Predict the Quality of Answers with Non-textual Features},
 year = {2006}
}

@article{jiang2014evolutionary,
 author = {Jiang, Chunxiao and Chen, Yan and Liu, KJ Ray},
 journal = {IEEE Transactions on Signal Processing},
 number = {17},
 pages = {4573--4586},
 publisher = {IEEE},
 title = {Evolutionary dynamics of information diffusion over social networks},
 volume = {62},
 year = {2014}
}

@inproceedings{jin2015mcg,
 author = {Jin, Zhiwei and Cao, Juan and Zhang, Yazi and Zhang, Yongdong},
 booktitle = {MediaEval},
 title = {MCG-ICT at MediaEval 2015: Verifying Multimedia Use with a Two-Level Classification Model.},
 year = {2015}
}

@inproceedings{jin2016news,
 author = {Zhiwei Jin and
Juan Cao and
Yongdong Zhang and
Jiebo Luo},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/JinCZL16.bib},
 booktitle = {Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence,
February 12-17, 2016, Phoenix, Arizona, {USA}},
 editor = {Dale Schuurmans and
Michael P. Wellman},
 pages = {2972--2978},
 publisher = {{AAAI} Press},
 timestamp = {Fri, 27 Nov 2020 00:00:00 +0100},
 title = {News Verification by Exploiting Conflicting Social Viewpoints in Microblogs},
 url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12128},
 year = {2016}
}

@article{jin2016novel,
 author = {Zhiwei Jin and Juan Cao and Yongdong Zhang and Jianshe Zhou and Qi Tian},
 journal={IEEE Transactions on Multimedia},
 nobibsource = {dblp computer science bibliography, https://dblp.org},
 nobiburl = {https://dblp.org/rec/bib/journals/tmm/JinCZZT17},
 nodoi = {10.1109/TMM.2016.2617078},
 notimestamp = {Sun, 28 May 2017 13:24:19 +0200},
 nourl = {https://doi.org/10.1109/TMM.2016.2617078},
 number = {3},
 pages = {598--608},
 title = {Novel Visual and Statistical Image Features for Microblogs News Verification},
 volume = {19},
 year = {2017}
}

@inproceedings{jin2017multimodal,
 author = {Jin, Zhiwei and Cao, Juan and Guo, Han and Zhang, Yongdong and Luo, Jiebo},
 booktitle={Proceedings of the 25th ACM international conference on Multimedia},
 pages = {795--816},
 title = {Multimodal fusion with recurrent neural networks for rumor detection on microblogs},
 year = {2017}
}

@inproceedings{Jin:2016:NVE:3016100.3016318,
 author = {Zhiwei Jin and
Juan Cao and
Yongdong Zhang and
Jiebo Luo},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/JinCZL16.bib},
 booktitle = {Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence,
February 12-17, 2016, Phoenix, Arizona, {USA}},
 editor = {Dale Schuurmans and
Michael P. Wellman},
 pages = {2972--2978},
 publisher = {{AAAI} Press},
 timestamp = {Fri, 27 Nov 2020 00:00:00 +0100},
 title = {News Verification by Exploiting Conflicting Social Viewpoints in Microblogs},
 url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12128},
 year = {2016}
}

@article{Jindal2020,
 author = {Jindal, Sarthak and Sood, Raghav and Singh, Richa and Vatsa, Mayank and Chakraborty, Tanmoy},
 issn = {16130073},
 journal = {CEUR Workshop Proceedings},
 pages = {138--145},
 title = {{NewsBag}: A multimodal benchmark dataset for fake news detection},
 volume = {2560},
 year = {2020}
}

@article{JMLR:v18:16-365,
 author = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
 journal = {Journal of Machine Learning Research},
 number = {17},
 pages = {1-5},
 title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
 url = {http://jmlr.org/papers/v18/16-365},
 volume = {18},
 year = {2017}
}

@incollection{Joachims:1999:MLS:299094.299104,
 acmid = {299104},
 address = {Cambridge, MA, USA},
 author = {Joachims, Thorsten},
 chapter = {Making Large-scale Support Vector Machine Learning Practical},
 editor = {Sch\"{o}lkopf, Bernhard and Burges, Christopher J. C. and Smola, Alexander J.},
 isbn = {0-262-19416-3},
 numpages = {16},
 pages = {169--184},
 publisher = {MIT Press},
 title = {Advances in Kernel Methods},
 year = {1999}
}

@inproceedings{Joachims:2002,
 acmid = {775067},
 address = {Edmonton, Alberta, Canada},
 author = {Joachims, Thorsten},
 booktitle = {Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 isbn = {1-58113-567-X},
 noaddress = {New York, NY, USA},
 nopublisher = {ACM},
 numpages = {10},
 pages = {133--142},
 series = {KDD '02},
 title = {Optimizing Search Engines Using Clickthrough Data},
 year = {2002}
}

@article{johnson2007every,
 author = {Johnson, Thomas J and Kaye, Barbara K and Bichard, Shannon L and Wong, W Joann},
 journal = {Journal of Computer-Mediated Communication},
 number = {1},
 pages = {100--122},
 publisher = {Wiley Online Library},
 title = {Every Blog Has Its Day: Politically-interested Internet Users’ Perceptions of Blog Credibility},
 volume = {13},
 year = {2007}
}

@inproceedings{johnson2015semi,
 author = {Rie Johnson and
Tong Zhang},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/JohnsonZ15.bib},
 booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference
on Neural Information Processing Systems 2015, December 7-12, 2015,
Montreal, Quebec, Canada},
 editor = {Corinna Cortes and
Neil D. Lawrence and
Daniel D. Lee and
Masashi Sugiyama and
Roman Garnett},
 pages = {919--927},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Semi-supervised Convolutional Neural Networks for Text Categorization
via Region Embedding},
 url = {https://proceedings.neurips.cc/paper/2015/hash/acc3e0404646c57502b480dc052c4fe1-Abstract.html},
 year = {2015}
}

@article{jolly2020psychometric,
 author = {Baani Leen Kaur Jolly and Palash Aggrawal and Amogh Gulati and Amarjit Singh Sethi and Ponnurangam Kumaraguru and Tavpritesh Sethi},
 journal = {arXiv:2005.05513},
 primaryclass = {cs.CL},
 title = {Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during {COVID-19} Infodemic},
 year = {2020}
}

@article{jotycodra,
 author = {Joty, Shafiq  and
Carenini, Giuseppe  and
Ng, Raymond T.},
 doi = {10.1162/COLI_a_00226},
 journal = {Computational Linguistics},
 number = {3},
 pages = {385--435},
 title = {{CODRA}: A Novel Discriminative Framework for Rhetorical Analysis},
 url = {https://www.aclweb.org/anthology/J15-3002},
 volume = {41},
 year = {2015}
}

@inproceedings{joulin2017bag,
 address = {Valencia, Spain},
 author = {Joulin, Armand  and
Grave, Edouard  and
Bojanowski, Piotr  and
Mikolov, Tomas},
 booktitle = {Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
 pages = {427--431},
 publisher = {Association for Computational Linguistics},
 title = {Bag of Tricks for Efficient Text Classification},
 url = {https://www.aclweb.org/anthology/E17-2068},
 year = {2017}
}

@article{journals/intr/CastilloMP13,
 author = {Castillo, Carlos and Mendoza, Marcelo and Poblete, Barbara},
 journal = {Internet Research},
 keywords = {dblp},
 nourl = {http://dblp.uni-trier.de/db/journals/intr/intr23.html#CastilloMP13},
 pages = {560-588},
 title = {Predicting information credibility in time-sensitive social media.}
}

@inproceedings{Jurczyk:2007:DAQ:1321440.1321575,
 acmid = {1321575},
 address = {Lisbon, Portugal},
 author = {Jurczyk, Pawel and Agichtein, Eugene},
 booktitle = {Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management},
 isbn = {978-1-59593-803-9},
 keywords = {link analysis, question-answer portals},
 noaddress = {New York, NY, USA},
 nopublisher = {ACM},
 numpages = {4},
 pages = {919--922},
 series = {CIKM '07},
 title = {Discovering Authorities in Question Answer Communities by Using Link Analysis},
 year = {2007}
}

@misc{kaggleCarl,
 author = {{Carlson}},
 howpublished = {\url{https://www.kaggle.com/carlsonhoo/coronavirus-tweets}},
 note = {Online; accessed 2020},
 title = {{Coronavirus Tweets, Tweets (json) for Coronavirus on Kaggle}}
}

@misc{kaggleShane,
 author = {{Shane Smith}},
 howpublished = {\url{https://www.kaggle.com/smid80/coronavirus-covid19-tweets-early-april}},
 note = {Online; accessed 2020},
 title = {{Coronavirus (covid19) Tweets - early April}}
}

@misc{kaggleSVEN,
 author = {{Sven Celin}},
 howpublished = {\url{https://www.kaggle.com/svencelin/covid19-tweets-afternoon-31032020}},
 note = {Online; accessed 2020},
 title = {{COVID-19 tweets afternoon 31.03.2020.}}
}

@article{KALIYAR202032,
 abstract = {With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36\% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.},
 author = {Rohit Kumar Kaliyar and Anurag Goswami and Pratik Narang and Soumendu Sinha},
 doi = {https://doi.org/10.1016/j.cogsys.2019.12.005},
 issn = {1389-0417},
 journal = {Cognitive Systems Research},
 keywords = {Fake news, Social media, Machine learning, Deep learning, Neural network},
 pages = {32 - 44},
 title = {FNDNet – A deep convolutional neural network for fake news detection},
 url = {http://www.sciencedirect.com/science/article/pii/S1389041720300085},
 volume = {61},
 year = {2020}
}

@inproceedings{kang2020multi,
 author = {Kang, SeongKu and Hwang, Junyoung and Yu, Hwanjo},
 booktitle={14th International Conference on Ubiquitous Information Management and Communication (IMCOM)},
 organization = {IEEE},
 pages = {1--6},
 title = {Multi-Modal Component Embedding for Fake News Detection},
 year = {2020}
}

@inproceedings{kapukaranov2015fine,
 address = {Hissar, Bulgaria},
 author = {Kapukaranov, Borislav  and
Nakov, Preslav},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing},
 pages = {266--274},
 publisher = {INCOMA Ltd. Shoumen, BULGARIA},
 title = {Fine-Grained Sentiment Analysis for Movie Reviews in {B}ulgarian},
 url = {https://www.aclweb.org/anthology/R15-1036},
 year = {2015}
}

@inproceedings{karadzhov-EtAl:2017:RANLP2,
 address = {Varna, Bulgaria},
 author = {Karadzhov, Georgi  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_046},
 pages = {344--353},
 publisher = {INCOMA Ltd.},
 title = {Fully Automated Fact Checking Using External Sources},
 url = {https://doi.org/10.26615/978-954-452-049-6_046},
 year = {2017}
}

@inproceedings{karadzhov2017fully,
 address = {Varna, Bulgaria},
 author = {Karadzhov, Georgi  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_046},
 pages = {344--353},
 publisher = {INCOMA Ltd.},
 title = {Fully Automated Fact Checking Using External Sources},
 url = {https://doi.org/10.26615/978-954-452-049-6_046},
 year = {2017}
}

@inproceedings{karadzhov2018we,
 address = {Varna, Bulgaria},
 author = {Karadzhov, Georgi  and
Gencheva, Pepa  and
Nakov, Preslav  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_045},
 pages = {334--343},
 publisher = {INCOMA Ltd.},
 title = {We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!},
 url = {https://doi.org/10.26615/978-954-452-049-6_045},
 year = {2017}
}

@article{karagiannis2020scrutinizer,
 author = {Georgios Karagiannis and Mohammed Saeed and Paolo Papotti and Immanuel Trummer},
 journal = {arXiv:2003.06708},
 primaryclass = {cs.DB},
 title = {Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification},
 year = {2020}
}

@article{karttunen1971implicative,
 author = {Karttunen, Lauri},
 journal = {Language},
 pages = {340--358},
 publisher = {JSTOR},
 title = {Implicative verbs},
 year = {1971}
}

@article{karttunen1971Implicatives,
 author = {Karttunen, Lauri},
 journal = {Language},
 number = {2},
 pages = {340--358},
 publisher = {Linguistic Society of America},
 title = {Implicative Verbs},
 volume = {47},
 year = {1971}
}

@inproceedings{kaya2016fusing,
 author = {Kaya, Heysem and Karpov, Alexey A},
 booktitle = {Interspeech},
 pages = {2046--2050},
 title = {Fusing Acoustic Feature Representations for Computational Paralinguistics Tasks.},
 volume = {2016},
 year = {2016}
}

@article{ketterer1997teaching,
 author = {Ketterer, Stan},
 journal = {Journalism \& Mass Communication Educator},
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 pages = {4--14},
 publisher = {SAGE Publications Sage CA: Los Angeles, CA},
 title = {Teaching students how to evaluate and use online resources},
 volume = {52},
 year = {1997}
}

@article{ketterer1998teaching,
 author = {Ketterer, Stan},
 journal = {Journalism \& Mass Communication Educator},
 number = {4},
 pages = {4},
 publisher = {Association for Education in Journalism and Mass Communication},
 title = {Teaching students how to evaluate and use online resources},
 volume = {52},
 year = {1998}
}

@inproceedings{khattar2019mvae,
 author = {Dhruv Khattar and
Jaipal Singh Goud and
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Fabrizio Silvestri and
Jie Tang},
 pages = {617--622},
 publisher = {{ACM}},
 timestamp = {Tue, 06 Nov 2018 00:00:00 +0100},
 title = {Careful what you share in six seconds: Detecting cyberbullying instances
in Vine},
 url = {https://doi.org/10.1145/2808797.2809381},
 year = {2015}
}

@article{ramisa2017breakingnews,
 author = {Ramisa, Arnau and Yan, Fei and Moreno-Noguer, Francesc and Mikolajczyk, Krystian},
 journal = {IEEE transactions on pattern analysis and machine intelligence},
 number = {5},
 pages = {1072--1085},
 publisher = {IEEE},
 title = {Breakingnews: Article annotation by image and text processing},
 volume = {40},
 year = {2017}
}

@inproceedings{RANLP2017:clickbait,
 address = {Varna, Bulgaria},
 author = {Karadzhov, Georgi  and
Gencheva, Pepa  and
Nakov, Preslav  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_045},
 pages = {334--343},
 publisher = {INCOMA Ltd.},
 title = {We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!},
 url = {https://doi.org/10.26615/978-954-452-049-6_045},
 year = {2017}
}

@inproceedings{RANLP2017:credibility:trolls,
 address = {Varna, Bulgaria},
 author = {Nakov, Preslav  and
Mihaylova, Tsvetomila  and
M{\`a}rquez, Llu{\'\i}s  and
Shiroya, Yashkumar  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_072},
 pages = {551--560},
 publisher = {INCOMA Ltd.},
 title = {Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums},
 url = {https://doi.org/10.26615/978-954-452-049-6_072},
 year = {2017}
}

@inproceedings{RANLP2017:debates,
 address = {Varna, Bulgaria},
 author = {Gencheva, Pepa  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_037},
 pages = {267--276},
 publisher = {INCOMA Ltd.},
 title = {A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates},
 url = {https://doi.org/10.26615/978-954-452-049-6_037},
 year = {2017}
}

@inproceedings{RANLP2017:factchecking:external,
 address = {Varna, Bulgaria},
 author = {Karadzhov, Georgi  and
Nakov, Preslav  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Koychev, Ivan},
 booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017},
 doi = {10.26615/978-954-452-049-6_046},
 pages = {344--353},
 publisher = {INCOMA Ltd.},
 title = {Fully Automated Fact Checking Using External Sources},
 url = {https://doi.org/10.26615/978-954-452-049-6_046},
 year = {2017}
}

@inproceedings{RANLP2019:checkworthiness:multitask,
 address = {Varna, Bulgaria},
 author = {Vasileva, Slavena  and
Atanasova, Pepa  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Nakov, Preslav},
 booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)},
 doi = {10.26615/978-954-452-056-4_141},
 pages = {1229--1239},
 publisher = {INCOMA Ltd.},
 title = {It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction},
 url = {https://www.aclweb.org/anthology/R19-1141},
 year = {2019}
}

@inproceedings{rashkin-EtAl:2017:EMNLP2017,
 address = {Copenhagen, Denmark},
 author = {Rashkin, Hannah  and
Choi, Eunsol  and
Jang, Jin Yea  and
Volkova, Svitlana  and
Choi, Yejin},
 booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D17-1317},
 pages = {2931--2937},
 publisher = {Association for Computational Linguistics},
 title = {Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking},
 url = {https://www.aclweb.org/anthology/D17-1317},
 year = {2017}
}

@article{rasooli-tetrault-2015,
 author = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
 journal = {Computing Research Repository},
 note = {version 2},
 title = {Yara Parser: {A} Fast and Accurate Dependency Parser},
 url = {http://arxiv.org/abs/1503.06733},
 volume = {arXiv:1503.06733},
 year = {2015}
}

@inproceedings{Recasens+al:13a,
 address = {Sofia, Bulgaria},
 author = {Recasens, Marta  and
Danescu-Niculescu-Mizil, Cristian  and
Jurafsky, Dan},
 booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
 pages = {1650--1659},
 publisher = {Association for Computational Linguistics},
 title = {Linguistic Models for Analyzing and Detecting Biased Language},
 url = {https://www.aclweb.org/anthology/P13-1162},
 year = {2013}
}

@inproceedings{recasens2013linguistic,
 address = {Sofia, Bulgaria},
 author = {Recasens, Marta  and
Danescu-Niculescu-Mizil, Cristian  and
Jurafsky, Dan},
 booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
 pages = {1650--1659},
 publisher = {Association for Computational Linguistics},
 title = {Linguistic Models for Analyzing and Detecting Biased Language},
 url = {https://www.aclweb.org/anthology/P13-1162},
 year = {2013}
}

@inproceedings{Recasens:ACL:13,
 address = {Sofia, Bulgaria},
 author = {Recasens, Marta  and
Danescu-Niculescu-Mizil, Cristian  and
Jurafsky, Dan},
 booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
 pages = {1650--1659},
 publisher = {Association for Computational Linguistics},
 title = {Linguistic Models for Analyzing and Detecting Biased Language},
 url = {https://www.aclweb.org/anthology/P13-1162},
 year = {2013}
}

@inproceedings{Reimers_2019,
 address = {Hong Kong, China},
 author = {Reimers, Nils  and
Gurevych, Iryna},
 booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
 doi = {10.18653/v1/D19-1410},
 pages = {3982--3992},
 publisher = {Association for Computational Linguistics},
 title = {Sentence-{BERT}: Sentence Embeddings using {S}iamese {BERT}-Networks},
 url = {https://www.aclweb.org/anthology/D19-1410},
 year = {2019}
}

@inproceedings{ribeiro2018characterizing,
 author = {Ribeiro, Manoel and Calais, Pedro and Santos, Yuri and Almeida, Virg{\'\i}lio and Meira Jr, Wagner},
 booktitle={Twelfth international AAAI conference on web and social media},
 number = {1},
 title = {Characterizing and detecting hateful users on {Twitter}},
 volume = {12},
 year = {2018}
}

@article{riedel2017simple,
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170703264R},
 archiveprefix = {arXiv},
 author = {Riedel, Benjamin and Augenstein, Isabelle and Spithourakis, Georgios P and Riedel, Sebastian},
 eprint = {1707.03264},
 journal = {ArXiv:1707.03264},
 keywords = {Computer Science - Computation and Language},
 primaryclass = {cs.CL},
 title = {A Simple but Tough-to-beat Baseline for the {Fake News Challenge} Stance Detection Task},
 year = {2017}
}

@inproceedings{riezler-EtAl:2007:ACLMain,
 address = {Prague, Czech Republic},
 author = {Riezler, Stefan  and
Vasserman, Alexander  and
Tsochantaridis, Ioannis  and
Mittal, Vibhu  and
Liu, Yi},
 booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
 pages = {464--471},
 publisher = {Association for Computational Linguistics},
 title = {Statistical Machine Translation for Query Expansion in Answer Retrieval},
 url = {https://www.aclweb.org/anthology/P07-1059},
 year = {2007}
}

@inproceedings{Riloff:2003:LEP:1119355.1119369,
 author = {Riloff, Ellen  and
Wiebe, Janyce},
 booktitle = {Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing},
 pages = {105--112},
 title = {Learning Extraction Patterns for Subjective Expressions},
 url = {https://www.aclweb.org/anthology/W03-1014},
 year = {2003}
}

@article{robbins1951stochastic,
 author = {Robbins, Herbert and Monro, Sutton},
 journal = {The annals of mathematical statistics},
 pages = {400--407},
 publisher = {JSTOR},
 title = {A stochastic approximation method},
 year = {1951}
}

@inproceedings{rosenthal-farra-nakov:2017:SemEval,
 address = {Atlanta, Georgia, USA},
 author = {Nakov, Preslav  and
Rosenthal, Sara  and
Kozareva, Zornitsa  and
Stoyanov, Veselin  and
Ritter, Alan  and
Wilson, Theresa},
 booktitle = {Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)},
 pages = {312--320},
 publisher = {Association for Computational Linguistics},
 title = {{S}em{E}val-2013 Task 2: Sentiment Analysis in {T}witter},
 url = {https://www.aclweb.org/anthology/S13-2052},
 year = {2013}
}

@inproceedings{rosenthal2017semeval,
 address = {Atlanta, Georgia, USA},
 author = {Nakov, Preslav  and
Rosenthal, Sara  and
Kozareva, Zornitsa  and
Stoyanov, Veselin  and
Ritter, Alan  and
Wilson, Theresa},
 booktitle = {Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)},
 pages = {312--320},
 publisher = {Association for Computational Linguistics},
 title = {{S}em{E}val-2013 Task 2: Sentiment Analysis in {T}witter},
 url = {https://www.aclweb.org/anthology/S13-2052},
 year = {2013}
}

@article{rothmann2003big,
 author = {Rothmann, Sebastiaan and Coetzer, Elize P},
 journal = {SA Journal of Industrial Psychology},
 number = {1},
 pages = {68--74},
 publisher = {AOSIS},
 title = {The big five personality dimensions and job performance},
 volume = {29},
 year = {2003}
}

@inproceedings{rubin2015deception,
 author = {Rubin, Victoria L and Chen, Yimin and Conroy, Niall J},
 booktitle = {Proceedings of the 78th ASIS\&T Annual Meeting: Information Science with Impact: Research in and for the Community},
 organization = {American Society for Information Science},
 pages = {83},
 title = {Deception detection for news: three types of fakes},
 year = {2015}
}

@inproceedings{rubin2016fake,
 address = {San Diego, California},
 author = {Rubin, Victoria  and
Conroy, Niall  and
Chen, Yimin  and
Cornwell, Sarah},
 booktitle = {Proceedings of the Second Workshop on Computational Approaches to Deception Detection},
 doi = {10.18653/v1/W16-0802},
 pages = {7--17},
 publisher = {Association for Computational Linguistics},
 title = {Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News},
 url = {https://www.aclweb.org/anthology/W16-0802},
 year = {2016}
}

@inproceedings{ruchansky2017csi,
 author = {Natali Ruchansky and
Sungyong Seo and
Yan Liu},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cikm/RuchanskySL17.bib},
 booktitle = {Proceedings of the 2017 {ACM} on Conference on Information and Knowledge
Management, {CIKM} 2017, Singapore, November 06 - 10, 2017},
 doi = {10.1145/3132847.3132877},
 editor = {Ee{-}Peng Lim and
Marianne Winslett and
Mark Sanderson and
Ada Wai{-}Chee Fu and
Jimeng Sun and
J. Shane Culpepper and
Eric Lo and
Joyce C. Ho and
Debora Donato and
Rakesh Agrawal and
Yu Zheng and
Carlos Castillo and
Aixin Sun and
Vincent S. Tseng and
Chenliang Li},
 pages = {797--806},
 publisher = {{ACM}},
 timestamp = {Wed, 18 Sep 2019 01:00:00 +0200},
 title = {{CSI:} {A} Hybrid Deep Model for Fake News Detection},
 url = {https://doi.org/10.1145/3132847.3132877},
 year = {2017}
}

@inproceedings{sabir2018deep,
 author = {Sabir, Ekraam and AbdAlmageed, Wael and Wu, Yue and Natarajan, Prem},
 booktitle = {Proceedings of the 26th ACM international conference on Multimedia},
 pages = {1337--1345},
 title = {Deep multimodal image-repurposing detection},
 year = {2018}
}

@inproceedings{sachan2019revisiting,
 author = {Devendra Singh Sachan and
Manzil Zaheer and
Ruslan Salakhutdinov},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/SachanZS19.bib},
 booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
2019, The Thirty-First Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
USA, January 27 - February 1, 2019},
 doi = {10.1609/aaai.v33i01.33016940},
 pages = {6940--6948},
 publisher = {{AAAI} Press},
 timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
 title = {Revisiting {LSTM} Networks for Semi-Supervised Text Classification
via Mixed Objective Function},
 url = {https://doi.org/10.1609/aaai.v33i01.33016940},
 year = {2019}
}

@article{SAHOO2021106983,
 abstract = {In recent years, the rise of Online Social Networks has led to proliferation of social news such as product advertisement, political news, celebrity’s information, etc. Some of the social networks such as Facebook, Instagram and Twitter affected by their user through fake news. Unfortunately, some users use unethical means to grow their links and reputation by spreading fake news in the form of texts, images, and videos. However, the recent information appearing on an online social network is doubtful, and in many cases, it misleads other users in the network. Fake news is spread intentionally to mislead readers to believe false news, which makes it difficult for detection mechanism to detect fake news on the basis of shared content. Therefore, we need to add some new information related to user’s profile, such as user’s involvement with others for finding a particular decision. The disseminated information and their diffusion process create a big problem for detecting these contents promptly and thus highlighting the need for automatic fake news detection. In this paper, we are going to introduce automatic fake news detection approach in chrome environment on which it can detect fake news on Facebook. Specifically, we use multiple features associated with Facebook account with some news content features to analyze the behavior of the account through deep learning. The experimental analysis of real-world information demonstrates that our intended fake news detection approach has achieved higher accuracy than the existing state of art techniques.},
 author = {Somya Ranjan Sahoo and B.B. Gupta},
 doi = {https://doi.org/10.1016/j.asoc.2020.106983},
 issn = {1568-4946},
 journal = {Applied Soft Computing},
 keywords = {Online Social Network, Fake news, Deep learning, Hybrid approach},
 pages = {106983},
 title = {Multiple features based approach for automatic fake news detection on social networks using deep learning},
 url = {http://www.sciencedirect.com/science/article/pii/S1568494620309224},
 volume = {100},
 year = {2021}
}

@book{salas:calculus,
 address = {New York},
 author = {S.L. Salas and Einar Hille},
 publisher = {John Wiley and Sons},
 title = {Calculus: One and Several Variable},
 year = {1978}
}

@inproceedings{Salway2005SomeIF,
 author = {A. Salway and R. Martinec},
 title = {Some Ideas for Modelling Image-Text Combinations},
 year = {2005}
}

@inproceedings{Schmidt2017survey,
 address = {Valencia, Spain},
 author = {Schmidt, Anna  and
Wiegand, Michael},
 booktitle = {Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media},
 doi = {10.18653/v1/W17-1101},
 pages = {1--10},
 publisher = {Association for Computational Linguistics},
 title = {A Survey on Hate Speech Detection using Natural Language Processing},
 url = {https://www.aclweb.org/anthology/W17-1101},
 year = {2017}
}

@inproceedings{schroff2015facenet,
 author = {Florian Schroff and
Dmitry Kalenichenko and
James Philbin},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cvpr/SchroffKP15.bib},
 booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2015, Boston, MA, USA, June 7-12, 2015},
 doi = {10.1109/CVPR.2015.7298682},
 pages = {815--823},
 publisher = {{IEEE}},
 timestamp = {Thu, 25 May 2017 01:00:00 +0200},
 title = {FaceNet: {A} unified embedding for face recognition and clustering},
 url = {https://doi.org/10.1109/CVPR.2015.7298682},
 year = {2015}
}

@inproceedings{schuller2011avec,
 acmid = {2062907},
 address = {Memphis, TN, USA},
 author = {Schuller, Bj{\"o}rn and Valstar, Michel and Eyben, Florian and McKeown, Gary and Cowie, Roddy and Pantic, Maja},
 booktitle = {Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction},
 isbn = {978-3-642-24570-1},
 keywords = {audiovisual emotion recognition, challenge, facial expression analysis, speech emotion recognition},
 noaddress = {Berlin, Heidelberg},
 nopublisher = {Springer-Verlag},
 nourl = {http://dl.acm.org/citation.cfm?id=2062850.2062907},
 numpages = {10},
 pages = {415--424},
 series = {ACI~'11},
 title = {{AVEC} 2011-the First International Audio/Visual Emotion Challenge},
 year = {2011}
}

@inproceedings{schuller2013interspeech,
 author = {Schuller, Bj{\"o}rn and Steidl, Stefan and Batliner, Anton and Vinciarelli, Alessandro and Scherer, Klaus and Ringeval, Fabien and Chetouani, Mohamed and Weninger, Felix and Eyben, Florian and Marchi, Erik and others},
 booktitle = {Interspeech},
 pages = {148--152},
 title = {The {Interspeech} 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism},
 year = {2013}
}

@inproceedings{schuller2016interspeech,
 author = {Schuller, Bj{\"o}rn and Steidl, Stefan and Batliner, Anton and Hirschberg, Julia and Burgoon, Judee K and Baird, Alice and Elkins, Aaron and Zhang, Yue and Coutinho, Eduardo and Evanini, Keelan and others},
 booktitle = {Interspeech},
 pages = {2001--2005},
 title = {The {Interspeech} 2016 computational paralinguistics challenge: Deception, sincerity \& native language},
 year = {2016}
}

@article{schwartz2012overview,
 author = {Schwartz, Shalom H},
 journal = {Online readings in Psychology and Culture},
 number = {1},
 pages = {2307--0919},
 title = {An overview of the Schwartz theory of basic values},
 volume = {2},
 year = {2012}
}

@article{scikit-learn,
 author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal = {Journal of Machine Learning Research},
 pages = {2825--2830},
 title = {Scikit-learn: Machine Learning in {P}ython},
 volume = {12},
 year = {2011}
}

@inproceedings{SemEval2021-6-Dimitrov,
 address = {Bangkok, Thailand},
 author = {Dimitrov, Dimitar and
Bin Ali, Bishr and
Shaar, Shaden and
Alam, Firoj and
Silvestri, Fabrizio and
Firooz, Hamed and
Nakov, Preslav and
Da San Martino, Giovanni},
 booktitle = {Proceedings of the 15th International Workshop on Semantic Evaluation},
 series = {SemEval~'21},
 title = {{Task 6 at SemEval-2021}: Detection of Persuasion Techniques in Texts and Images},
 year = {2021},
 publisher = {Association for Computational Linguistics},
}

@inproceedings{SeminarUsers2017,
 address = {Oxford, UK},
 author = {Kareem Darwish and
Dimitar Alexandrov and
Preslav Nakov and
Yelena Mejova},
 booktitle = {Proceedings of the 9th International Conference on Social Informatics},
 nobibsource = {dblp computer science bibliography, http://dblp.org},
 nobiburl = {http://dblp.uni-trier.de/rec/bib/conf/socinfo/DarwishANM17},
 nodoi = {10.1007/978-3-319-67217-5_7},
 notimestamp = {Wed, 06 Sep 2017 08:49:37 +0200},
 nourl = {https://doi.org/10.1007/978-3-319-67217-5_7},
 pages = {91--108},
 series = {SocInfo~'17},
 title = {Seminar Users in the {A}rabic {T}witter Sphere},
 year = {2017}
}

@article{seo2014visual,
 author = {Hyunjin Seo},
 journal = {Visual Communication Quarterly},
 nodoi = {10.1080/15551393.2014.955501},
 noeprint = {https://doi.org/10.1080/15551393.2014.955501},
 nourl = {https://doi.org/10.1080/15551393.2014.955501},
 number = {3},
 pages = {150-161},
 publisher = {Routledge},
 title = {{Visual propaganda in the age of social media: An empirical analysis of {Twitter} images during the 2012 {Israeli–Hamas} conflict}},
 volume = {21},
 year = {2014}
}

@inproceedings{severyn2015sigir,
 author = {Aliaksei Severyn and
Alessandro Moschitti},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/sigir/SeverynM15.bib},
 booktitle = {Proceedings of the 38th International {ACM} {SIGIR} Conference on
Research and Development in Information Retrieval, Santiago, Chile,
August 9-13, 2015},
 doi = {10.1145/2766462.2767738},
 editor = {Ricardo Baeza{-}Yates and
Mounia Lalmas and
Alistair Moffat and
Berthier A. Ribeiro{-}Neto},
 pages = {373--382},
 publisher = {{ACM}},
 timestamp = {Mon, 26 Nov 2018 00:00:00 +0100},
 title = {Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks},
 url = {https://doi.org/10.1145/2766462.2767738},
 year = {2015}
}

@inproceedings{shaar2020known,
 address = {Online},
 author = {Shaar, Shaden  and
Babulkov, Nikolay  and
Da San Martino, Giovanni  and
Nakov, Preslav},
 booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
 doi = {10.18653/v1/2020.acl-main.332},
 pages = {3607--3618},
 series = {ACL~'20},
 publisher = {Association for Computational Linguistics},
 title = {That is a Known Lie: Detecting Previously Fact-Checked Claims},
 url = {https://www.aclweb.org/anthology/2020.acl-main.332},
 year = {2020}
}

@article{Shah2020,
 author = {Shah, Priyanshi and Kobti, Ziad},
 doi = {10.1109/CEC48606.2020.9185643},
 isbn = {9781728169293},
 journal = {2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings},
 keywords = {Cultural algorithm,Fake news detection,Segmentation process,Sentiment analysis},
 title = {Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge},
 year = {2020}
}

@article{shahi2020amused,
 author = {Shahi, Gautam Kishore},
 journal = {arXiv preprint arXiv:2010.00502},
 title = {AMUSED: An Annotation Framework of Multi-modal Social Media Data},
 year = {2020}
}

@article{shahi2020exploratory,
 author = {Shahi, Gautam Kishore and Dirkson, Anne and Majchrzak, Tim A},
 journal = {arXiv preprint arXiv:2005.05710},
 title = {An Exploratory Study of COVID-19 Misinformation on Twitter},
 year = {2020}
}

@article{shahi2020fakecovid,
 author = {Shahi, Gautam Kishore and Nandini, Durgesh},
 journal = {arXiv preprint arXiv:2006.11343},
 title = {{FakeCovid}--A Multilingual Cross-domain Fact Check News Dataset for {COVID-19}},
 year = {2020}
}

@inproceedings{shahifakecovid,
 author = {Shahi, Gautam Kishore and Nandini, Durgesh},
 booktitle = {Workshop Proceedings of the 14th International {AAAI} {C}onference on {W}eb and {S}ocial {M}edia},
 title = {Fake{C}ovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19},
 url = {http://workshop-proceedings.icwsm.org/pdf/2020_14.pdf},
 year = {2020}
}

@inproceedings{shao2016hoaxy,
 author = {Shao, Chengcheng and Ciampaglia, Giovanni Luca and Flammini, Alessandro and Menczer, Filippo},
 booktitle = {Proceedings of the 25th international conference companion on world wide web},
 pages = {745--750},
 title = {Hoaxy: A platform for tracking online misinformation},
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 author = {Tangcharoensathien, Viroj and Calleja, Neville and Nguyen, Tim and Purnat, Tina and D’Agostino, Marcelo and Garcia-Saiso, Sebastian and Landry, Mark and Rashidian, Arash and Hamilton, Clayton and AbdAllah, Abdelhalim and others},
 journal = {Journal of medical Internet research},
 number = {6},
 pages = {e19659},
 publisher = {JMIR Publications Inc., Toronto, Canada},
 title = {Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation},
 volume = {22},
 year = {2020}
}

@inproceedings{taniguchi-etal-2015-weighted,
 address = {Lisbon, Portugal},
 author = {Taniguchi, Tomoki  and
Sakaki, Shigeyuki  and
Shigenaka, Ryosuke  and
Tsuboshita, Yukihiro  and
Ohkuma, Tomoko},
 booktitle = {Proceedings of the Fourth Workshop on Vision and Language},
 doi = {10.18653/v1/W15-2814},
 pages = {87--93},
 publisher = {Association for Computational Linguistics},
 title = {A Weighted Combination of Text and Image Classifiers for User Gender Inference},
 url = {https://www.aclweb.org/anthology/W15-2814},
 year = {2015}
}

@inproceedings{tarvainen2017mean,
 author = {Antti Tarvainen and
Harri Valpola},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/TarvainenV17.bib},
 booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference
on Neural Information Processing Systems 2017, December 4-9, 2017,
Long Beach, CA, {USA}},
 editor = {Isabelle Guyon and
Ulrike von Luxburg and
Samy Bengio and
Hanna M. Wallach and
Rob Fergus and
S. V. N. Vishwanathan and
Roman Garnett},
 pages = {1195--1204},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results},
 url = {https://proceedings.neurips.cc/paper/2017/hash/68053af2923e00204c3ca7c6a3150cf7-Abstract.html},
 year = {2017}
}

@misc{TeXFAQ,
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 address = {Minneapolis, Minnesota},
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Chang, Ming-Wei  and
Lee, Kenton  and
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 doi = {10.18653/v1/N19-1423},
 pages = {4171--4186},
 publisher = {Association for Computational Linguistics},
 title = {{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding},
 url = {https://www.aclweb.org/anthology/N19-1423},
 year = {2019}
}

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}

@thesis{thesis_malala,
 author = {Ruly Kartika},
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@inproceedings{thorne-etal-2019-fever2,
 address = {Hong Kong, China},
 author = {Thorne, James  and
Vlachos, Andreas  and
Cocarascu, Oana  and
Christodoulopoulos, Christos  and
Mittal, Arpit},
 booktitle = {Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)},
 doi = {10.18653/v1/D19-6601},
 pages = {1--6},
 publisher = {Association for Computational Linguistics},
 title = {The {FEVER}2.0 Shared Task},
 url = {https://www.aclweb.org/anthology/D19-6601},
 year = {2019}
}

@inproceedings{thorne-EtAl:2017:NLPmJ,
 address = {Copenhagen, Denmark},
 author = {Thorne, James  and
Chen, Mingjie  and
Myrianthous, Giorgos  and
Pu, Jiashu  and
Wang, Xiaoxuan  and
Vlachos, Andreas},
 booktitle = {Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism},
 doi = {10.18653/v1/W17-4214},
 pages = {80--83},
 publisher = {Association for Computational Linguistics},
 title = {Fake news stance detection using stacked ensemble of classifiers},
 url = {https://www.aclweb.org/anthology/W17-4214},
 year = {2017}
}

@inproceedings{thorne-EtAl:2018:N18-1,
 address = {New Orleans, Louisiana},
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Vlachos, Andreas  and
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 doi = {10.18653/v1/N18-1074},
 pages = {809--819},
 publisher = {Association for Computational Linguistics},
 title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification},
 url = {https://www.aclweb.org/anthology/N18-1074},
 year = {2018}
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 title = {Automated Fact Checking: Task Formulations, Methods and Future Directions},
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 year = {2018}
}

@inproceedings{thorne2018fever,
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Vlachos, Andreas  and
Christodoulopoulos, Christos  and
Mittal, Arpit},
 booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
 doi = {10.18653/v1/N18-1074},
 pages = {809--819},
 publisher = {Association for Computational Linguistics},
 title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification},
 url = {https://www.aclweb.org/anthology/N18-1074},
 year = {2018}
}

@misc{ti-data,
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@inproceedings{tran-EtAl:2015:SemEval,
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 doi = {10.18653/v1/S15-2038},
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 publisher = {Association for Computational Linguistics},
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}

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@misc{twitter,
 author = {{Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J and Wong, Kam-Fai and Cha, Meeyoung}},
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@incollection{ukkonen1993approximate,
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@article{Umair2020geocovid19,
 address = {New York, NY, USA},
 author = {Qazi, Umair and Imran, Muhammad and Ofli, Ferda},
 issue_date = {March 2020},
 journal = {SIGSPATIAL Special},
 nodoi = {10.1145/3404111.3404114},
 nourl = {https://doi.org/10.1145/3404111.3404114},
 number = {1},
 numpages = {10},
 pages = {6–15},
 publisher = {Association for Computing Machinery},
 title = {{GeoCoV19}: A Dataset of Hundreds of Millions of Multilingual {COVID}-19 Tweets with Location Information},
 volume = {12},
 year = {2020}
}

@article{vaezi2020infodemic,
 author = {Vaezi, Atefeh and Javanmard, Shaghayegh Haghjooy},
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 year = {2020}
}

@article{van2020seeking,
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 year = {2020}
}

@article{vargas2020detection,
 author = {Luis Vargas and Patrick Emami and Patrick Traynor},
 journal = {arXiv:2005.13466},
 primaryclass = {cs.SI},
 title = {On the Detection of Disinformation Campaign Activity with Network Analysis},
 year = {2020}
}

@inproceedings{varol2017online,
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 year = {2017}
}

@article{varshney2020hoax,
 author = {Varshney, Deepika and Vishwakarma, Dinesh Kumar},
 journal = {Journal of Ambient Intelligence and Humanized Computing},
 pages = {1--14},
 publisher = {Springer},
 title = {Hoax news-inspector: a real-time prediction of fake news using content resemblance over web search results for authenticating the credibility of news articles},
 year = {2020}
}

@article{varshney2020review,
 author = {Varshney, Deepika and Vishwakarma, Dinesh Kumar},
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 pages = {114208},
 publisher = {Elsevier},
 title = {{\color{black}A Review on Rumour Prediction and Veracity Assessment in Online Social Network}},
 year = {2020}
}

@article{varshneyunified,
 author = {Varshney, Deepika and Vishwakarma, Dinesh Kumar},
 journal = {Applied Intelligence},
 pages = {1--22},
 publisher = {Springer},
 title = {A unified approach for detection of Clickbait videos on YouTube using cognitive evidences}
}

@inproceedings{vasileva2019takes,
 address = {Varna, Bulgaria},
 author = {Vasileva, Slavena  and
Atanasova, Pepa  and
M{\`a}rquez, Llu{\'\i}s  and
Barr{\'o}n-Cede{\~n}o, Alberto  and
Nakov, Preslav},
 booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)},
 doi = {10.26615/978-954-452-056-4_141},
 pages = {1229--1239},
 publisher = {INCOMA Ltd.},
 title = {It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction},
 url = {https://www.aclweb.org/anthology/R19-1141},
 year = {2019}
}

@article{vicario2019polarization,
 author = {Vicario, Michela Del and Quattrociocchi, Walter and Scala, Antonio and Zollo, Fabiana},
 journal = {ACM Transactions on the Web (TWEB)},
 number = {2},
 pages = {1--22},
 publisher = {ACM New York, NY, USA},
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 volume = {13},
 year = {2019}
}

@inproceedings{vidgen2020detecting,
 address = {Online},
 author = {Vidgen, Bertie  and
Hale, Scott  and
Guest, Ella  and
Margetts, Helen  and
Broniatowski, David  and
Waseem, Zeerak  and
Botelho, Austin  and
Hall, Matthew  and
Tromble, Rebekah},
 booktitle = {Proceedings of the Fourth Workshop on Online Abuse and Harms},
 doi = {10.18653/v1/2020.alw-1.19},
 pages = {162--172},
 publisher = {Association for Computational Linguistics},
 title = {Detecting {E}ast {A}sian Prejudice on Social Media},
 url = {https://www.aclweb.org/anthology/2020.alw-1.19},
 year = {2020}
}



@article{Vijayaraghavan2019multimodalhate,
  title={Interpretable multi-modal hate speech detection},
  author={Vijayaraghavan, Prashanth and Larochelle, Hugo and Roy, Deb},
  journal={arXiv preprint arXiv:2103.01616},
  year={2021}
}

@article{vinyals2016show,
 author = {Vinyals, Oriol and Toshev, Alexander and Bengio, Samy and Erhan, Dumitru},
 journal = {IEEE transactions on pattern analysis and machine intelligence},
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 pages = {652--663},
 publisher = {IEEE},
 title = {Show and tell: Lessons learned from the 2015 mscoco image captioning challenge},
 volume = {39},
 year = {2016}
}

@article{vishwakarma2019detection,
 author = {Vishwakarma, Dinesh Kumar and Varshney, Deepika and Yadav, Ashima},
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 pages = {217--229},
 publisher = {Elsevier},
 title = {Detection and veracity analysis of fake news via scrapping and authenticating the web search},
 volume = {58},
 year = {2019}
}

@inproceedings{vishwakarma2020recent,
 author = {Vishwakarma, Dinesh Kumar and Jain, Chhavi},
 booktitle = {2020 International Conference for Emerging Technology (INCET)},
 organization = {IEEE},
 pages = {1--6},
 title = {{\color{black}Recent State-of-the-art of Fake News Detection: A Review}},
 year = {2020}
}

@inproceedings{vlachos-riedel:2014:W14-25,
 address = {Baltimore, MD, USA},
 author = {Vlachos, Andreas  and
Riedel, Sebastian},
 booktitle = {Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science},
 doi = {10.3115/v1/W14-2508},
 pages = {18--22},
 publisher = {Association for Computational Linguistics},
 title = {Fact Checking: Task definition and dataset construction},
 url = {https://www.aclweb.org/anthology/W14-2508},
 year = {2014}
}

@inproceedings{vlachos-riedel:2015:EMNLP,
 address = {Lisbon, Portugal},
 author = {Vlachos, Andreas  and
Riedel, Sebastian},
 booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
 doi = {10.18653/v1/D15-1312},
 pages = {2596--2601},
 publisher = {Association for Computational Linguistics},
 title = {Identification and Verification of Simple Claims about Statistical Properties},
 url = {https://www.aclweb.org/anthology/D15-1312},
 year = {2015}
}

@inproceedings{vlachos2014fact,
 address = {Baltimore, MD, USA},
 author = {Vlachos, Andreas  and
Riedel, Sebastian},
 booktitle = {Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science},
 doi = {10.3115/v1/W14-2508},
 pages = {18--22},
 publisher = {Association for Computational Linguistics},
 title = {Fact Checking: Task definition and dataset construction},
 url = {https://www.aclweb.org/anthology/W14-2508},
 year = {2014}
}

@inproceedings{vo2018rise,
 author = {Nguyen Vo and
Kyumin Lee},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/sigir/VoL18.bib},
 booktitle = {The 41st International {ACM} {SIGIR} Conference on Research {\&} Development
in Information Retrieval, {SIGIR} 2018, Ann Arbor, MI, USA, July 08-12,
2018},
 doi = {10.1145/3209978.3210037},
 editor = {Kevyn Collins{-}Thompson and
Qiaozhu Mei and
Brian D. Davison and
Yiqun Liu and
Emine Yilmaz},
 pages = {275--284},
 publisher = {{ACM}},
 timestamp = {Wed, 21 Nov 2018 00:00:00 +0100},
 title = {The Rise of Guardians: Fact-checking {URL} Recommendation to Combat
Fake News},
 url = {https://doi.org/10.1145/3209978.3210037},
 year = {2018}
}

@inproceedings{Volkova_Ayton_Arendt_Huang_Hutchinson_2019,
 author = {Volkova, Svitlana and Ayton, Ellyn and Arendt, Dustin L and Huang, Zhuanyi and Hutchinson, Brian},
 booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
 number = {01},
 pages = {659--662},
 title = {Explaining multimodal deceptive news prediction models},
 volume = {13},
 year = {2019}
}


@article{Vosoughi1146,
 author = {Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
 issn = {0036-8075},
 journal = {Science},
 nodoi = {10.1126/science.aap9559},
 noeprint = {http://science.sciencemag.org/content/359/6380/1146.full.pdf},
 nopublisher = {American Association for the Advancement of Science},
 nourl = {http://science.sciencemag.org/content/359/6380/1146},
 number = {6380},
 pages = {1146--1151},
 title = {The spread of true and false news online},
 volume = {359},
 year = {2018}
}

@article{vosoughi2017rumor,
 author = {Vosoughi, Soroush and Mohsenvand, Mostafa ‘Neo’ and Roy, Deb},
 journal={ACM transactions on knowledge discovery from data (TKDD)},
 number = {4},
 pages = {1--36},
 publisher = {ACM},
 title = {Rumor gauge: Predicting the veracity of rumors on {Twitter}},
 volume = {11},
 year = {2017}
}

@article{vosoughi2018spread,
 author = {Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
 journal = {Science},
 number = {6380},
 pages = {1146--1151},
 publisher = {American Association for the Advancement of Science},
 title = {The spread of true and false news online},
 volume = {359},
 year = {2018}
}

@article{vosoughi2018spread1,
 author = {Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
 journal = {Science},
 number = {6380},
 pages = {1146--1151},
 publisher = {American Association for the Advancement of Science},
 title = {The spread of true and false news online},
 volume = {359},
 year = {2018}
}

@article{vosoughi2018spread2,
 author = {Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
 journal = {Science},
 number = {6380},
 pages = {1146--1151},
 publisher = {American Association for the Advancement of Science},
 title = {The spread of true and false news online},
 volume = {359},
 year = {2018}
}

@inproceedings{wang-nyberg:2015:ACL-IJCNLP,
 address = {Beijing, China},
 author = {Wang, Di  and
Nyberg, Eric},
 booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
 doi = {10.3115/v1/P15-2116},
 pages = {707--712},
 publisher = {Association for Computational Linguistics},
 title = {A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering},
 url = {https://www.aclweb.org/anthology/P15-2116},
 year = {2015}
}

@inproceedings{wang2011information,
 author = {Dashun Wang and
Zhen Wen and
Hanghang Tong and
Ching{-}Yung Lin and
Chaoming Song and
Albert{-}L{\'{a}}szl{\'{o}} Barab{\'{a}}si},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/www/WangWTLSB11.bib},
 booktitle = {Proceedings of the 20th International Conference on World Wide Web,
{WWW} 2011, Hyderabad, India, March 28 - April 1, 2011},
 doi = {10.1145/1963405.1963508},
 editor = {Sadagopan Srinivasan and
Krithi Ramamritham and
Arun Kumar and
M. P. Ravindra and
Elisa Bertino and
Ravi Kumar},
 pages = {735--744},
 publisher = {{ACM}},
 timestamp = {Tue, 06 Nov 2018 00:00:00 +0100},
 title = {Information spreading in context},
 url = {https://doi.org/10.1145/1963405.1963508},
 year = {2011}
}

@inproceedings{wang2017liar,
 address = {Vancouver, Canada},
 author = {Wang, William Yang},
 booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
 doi = {10.18653/v1/P17-2067},
 pages = {422--426},
 publisher = {Association for Computational Linguistics},
 title = {{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection},
 url = {https://www.aclweb.org/anthology/P17-2067},
 year = {2017}
}

@inproceedings{wang2018eann,
 author = {Yaqing Wang and
Fenglong Ma and
Zhiwei Jin and
Ye Yuan and
Guangxu Xun and
Kishlay Jha and
Lu Su and
Jing Gao},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/kdd/WangMJYXJSG18.bib},
 booktitle = {Proceedings of the 24th {ACM} {SIGKDD} International Conference on
Knowledge Discovery {\&} Data Mining, {KDD} 2018, London, UK, August
19-23, 2018},
 doi = {10.1145/3219819.3219903},
 editor = {Yike Guo and
Faisal Farooq},
 pages = {849--857},
 publisher = {{ACM}},
 timestamp = {Tue, 09 Jul 2019 01:00:00 +0200},
 title = {{EANN:} Event Adversarial Neural Networks for Multi-Modal Fake News
Detection},
 url = {https://doi.org/10.1145/3219819.3219903},
 year = {2018}
}

@inproceedings{Wang2018TheCT,
 address = {Avignon, France},
 author = {Dongsheng Wang and Jakob Grue Simonsen and Birger Larsen and Christina Lioma},
 booktitle = {Working Notes of CLEF 2018},
 series = {CLEF~'18},
 title = {The {C}openhagen Team Participation in the Factuality Task of the Competition of Automatic Identification and Verification of Claims in Political Debates of the {CLEF}-2018 Fact Checking Lab},
 year = {2018}
}



@article{wang2020understanding, 
title={Understanding the Use of Fauxtography on Social Media}, volume={15}, url={https://ojs.aaai.org/index.php/ICWSM/article/view/18102}, abstractNote={Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation.}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Wang, Yuping and Tahmasbi, Fatemeh and Blackburn, Jeremy and Bradlyn, Barry and De Cristofaro, Emiliano and Magerman, David and Zannettou, Savvas and Stringhini, Gianluca}, year={2021}, month={May}, pages={776-786} }

@inproceedings{wang:2017:Short,
 address = {Vancouver, Canada},
 author = {Wang, William Yang},
 booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
 doi = {10.18653/v1/P17-2067},
 pages = {422--426},
 publisher = {Association for Computational Linguistics},
 title = {{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection},
 url = {https://www.aclweb.org/anthology/P17-2067},
 year = {2017}
}

@inproceedings{Waseem2017understanding,
 address = {Vancouver, BC, Canada},
 author = {Waseem, Zeerak  and
Davidson, Thomas  and
Warmsley, Dana  and
Weber, Ingmar},
 booktitle = {Proceedings of the First Workshop on Abusive Language Online},
 doi = {10.18653/v1/W17-3012},
 pages = {78--84},
 publisher = {Association for Computational Linguistics},
 title = {Understanding Abuse: A Typology of Abusive Language Detection Subtasks},
 url = {https://www.aclweb.org/anthology/W17-3012},
 year = {2017}
}

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 author = {Hobbs, Renee},
 journal = {Media Education Lab},
 title = {Mind Over Media: Analyzing Contemporary Propaganda.”},
 year = {2015}
}

@misc{web_post,
 author = {Belicove, Mikal E.},
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@article{zhou2019network,
 title={Network-based fake news detection: A pattern-driven approach},
  author={Zhou, Xinyi and Zafarani, Reza},
  journal={ACM SIGKDD explorations newsletter},
  volume={21},
  number={2},
  pages={48--60},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{zhou2020,
 author = {Zhou, Xinyi and Wu, Jindi and Zafarani, Reza},
 booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 organization = {Springer},
 pages = {354--367},
 title = {SAFE: Similarity-Aware Multi-modal Fake News Detection},
 year = {2020}
}

@inproceedings{Zhou2020a,
 author = {Xinyi Zhou and
Apurva Mulay and
Emilio Ferrara and
Reza Zafarani},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cikm/ZhouMFZ20.bib},
 booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
 doi = {10.1145/3340531.3412880},
 editor = {Mathieu d'Aquin and
Stefan Dietze and
Claudia Hauff and
Edward Curry and
Philippe Cudr{\'{e}}{-}Mauroux},
 pages = {3205--3212},
 publisher = {{ACM}},
 timestamp = {Mon, 19 Oct 2020 01:00:00 +0200},
 title = {ReCOVery: {A} Multimodal Repository for {COVID-19} News Credibility
Research},
 url = {https://doi.org/10.1145/3340531.3412880},
 year = {2020}
}

@inproceedings{zhou2020multimodal,
 author = {Zhou, Xinyi and Wu, Jindi and Zafarani, Reza},
 booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 organization = {Springer},
 pages = {354--367},
 title = {{SAFE}: Similarity-Aware Multi-modal Fake News Detection},
 year = {2020}
}

@inproceedings{zhou2020repository,
 author = {Xinyi Zhou and
Apurva Mulay and
Emilio Ferrara and
Reza Zafarani},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/cikm/ZhouMFZ20.bib},
 booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
 doi = {10.1145/3340531.3412880},
 editor = {Mathieu d'Aquin and
Stefan Dietze and
Claudia Hauff and
Edward Curry and
Philippe Cudr{\'{e}}{-}Mauroux},
 pages = {3205--3212},
 publisher = {{ACM}},
 timestamp = {Mon, 19 Oct 2020 01:00:00 +0200},
 title = {ReCOVery: {A} Multimodal Repository for {COVID-19} News Credibility
Research},
 url = {https://doi.org/10.1145/3340531.3412880},
 year = {2020}
}

@inproceedings{zhou2020safe,
 author = {Zhou, Xinyi and Wu, Jindi and Zafarani, Reza},
 booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 organization = {Springer},
 pages = {354--367},
 title = {{SAFE}: Similarity-Aware Multi-modal Fake News Detection},
 year = {2020}
}

@article{zhou2020survey,
 author = {Zhou, Xinyi and Zafarani, Reza},
 journal = {ACM Computing Surveys (CSUR)},
 number = {5},
 pages = {1--40},
 publisher = {ACM New York, NY, USA},
 title = {A survey of fake news: Fundamental theories, detection methods, and opportunities},
 volume = {53},
 year = {2020}
}

@inproceedings{zhu2003semi,
 author = {Xiaojin Zhu and
Zoubin Ghahramani and
John D. Lafferty},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/ZhuGL03.bib},
 booktitle = {Machine Learning, Proceedings of the Twentieth International Conference
{(ICML} 2003), August 21-24, 2003, Washington, DC, {USA}},
 editor = {Tom Fawcett and
Nina Mishra},
 pages = {912--919},
 publisher = {{AAAI} Press},
 timestamp = {Tue, 12 Jul 2016 01:00:00 +0200},
 title = {Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions},
 url = {http://www.aaai.org/Library/ICML/2003/icml03-118.php},
 year = {2003}
}

@inproceedings{zhu2015sematch,
 address = {Portoroz, Slovenia},
 author = {Ganggao Zhu and
Carlos Angel Iglesias},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/bib/conf/esws/ZhuI15},
 booktitle = {Joint Proceedings of the 1st International Workshop on Summarizing
and Presenting Entities and Ontologies and the 3rd International Workshop
on Human Semantic Web Interfaces},
 crossref = {DBLP:conf/esws/2015sumpre-hswi},
 nourl = {http://ceur-ws.org/Vol-1556/paper2.pdf},
 timestamp = {Tue, 28 May 2019 16:23:27 +0200},
 title = {Sematch: Semantic Entity Search from Knowledge Graph},
 year = {2015}
}

@article{zhu2016computing,
 author = {Zhu, Ganggao and Iglesias, Carlos A},
 journal = {IEEE Transactions on Knowledge and Data Engineering},
 number = {1},
 pages = {72--85},
 publisher = {IEEE},
 title = {Computing semantic similarity of concepts in knowledge graphs},
 volume = {29},
 year = {2016}
}

@inproceedings{zhu2019hierarchical,
 author = {Ming Zhu and
Aman Ahuja and
Wei Wei and
Chandan K. Reddy},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/www/ZhuA0R19.bib},
 booktitle = {The World Wide Web Conference, {WWW} 2019, San Francisco, CA, USA,
May 13-17, 2019},
 doi = {10.1145/3308558.3313699},
 editor = {Ling Liu and
Ryen W. White and
Amin Mantrach and
Fabrizio Silvestri and
Julian J. McAuley and
Ricardo Baeza{-}Yates and
Leila Zia},
 pages = {2472--2482},
 publisher = {{ACM}},
 timestamp = {Thu, 16 May 2019 01:00:00 +0200},
 title = {A Hierarchical Attention Retrieval Model for Healthcare Question Answering},
 url = {https://doi.org/10.1145/3308558.3313699},
 year = {2019}
}

@article{zou2005regularization,
 author = {Zou, Hui and Hastie, Trevor},
 journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
 number = {2},
 pages = {301--320},
 publisher = {Wiley Online Library},
 title = {Regularization and variable selection via the elastic net},
 volume = {67},
 year = {2005}
}

@article{Zubiaga2014,
 author = {Zubiaga, Arkaitz
and Ji, Heng},
 issn = {1869-5469},
 journal = {SNAM},
 nodoi = {10.1007/s13278-014-0163-y},
 nourl = {http://dx.doi.org/10.1007/s13278-014-0163-y},
 number = {1},
 pages = {1--12},
 title = {Tweet, but verify: epistemic study of information verification on {T}witter},
 volume = {4},
 year = {2014}
}

@article{zubiaga2015analysing,
 author = {Zubiaga, Arkaitz and Hoi, Geraldine Wong Sak and Liakata, Maria and Procter, Rob and Tolmie, Peter},
 journal = {arXiv:1511.07487},
 title = {Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads},
 year = {2015}
}

@inproceedings{zubiaga2015towards,
 author = {Zubiaga, Arkaitz and Liakata, Maria and Procter, Rob and Bontcheva, Kalina and Tolmie, Peter},
 booktitle = {Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence},
 title = {Towards detecting rumours in social media},
 year = {2015}
}

@article{zubiaga2016analysing,
 author = {Zubiaga, Arkaitz and Liakata, Maria and Procter, Rob and Hoi, Geraldine Wong Sak and Tolmie, Peter},
 journal = {PloS one},
 number = {3},
 pages = {e0150989},
 publisher = {Public Library of Science},
 title = {Analysing how people orient to and spread rumours in social media by looking at conversational threads},
 volume = {11},
 year = {2016}
}

@article{zubiaga2016analysing,
 author = {Zubiaga, Arkaitz and Liakata, Maria and Procter, Rob and Hoi, Geraldine Wong Sak and Tolmie, Peter},
 journal = {PloS one},
 number = {3},
 pages = {e0150989},
 publisher = {Public Library of Science},
 title = {Analysing how people orient to and spread rumours in social media by looking at conversational threads},
 volume = {11},
 year = {2016}
}

@article{zubiaga2018,
 author = {Zubiaga, Arkaitz and Aker, Ahmet and Bontcheva, Kalina and Liakata, Maria and Procter, Rob},
 journal = {ACM Computing Surveys (CSUR)},
 number = {2},
 pages = {32},
 publisher = {ACM},
 title = {Detection and resolution of rumours in social media: A survey},
 volume = {51},
 year = {2018}
}

@article{zubiaga2018detection,
 author = {Zubiaga, Arkaitz and Aker, Ahmet and Bontcheva, Kalina and Liakata, Maria and Procter, Rob},
 journal = {ACM Computing Surveys (CSUR)},
 number = {2},
 pages = {1--36},
 publisher = {ACM New York, NY, USA},
 title = {Detection and resolution of rumours in social media: A survey},
 volume = {51},
 year = {2018}
}

@article{ZubiagaKLPLBCA18,
 author = {Zubiaga, Arkaitz and Kochkina, Elena and Liakata, Maria and Procter, Rob and Lukasik, Michal and Bontcheva, Kalina and Cohn, Trevor and Augenstein, Isabelle},
 journal = {Inf. Process. Manage.},
 nodoi = {10.1016/j.ipm.2017.11.009},
 nourl = {https://doi.org/10.1016/j.ipm.2017.11.009},
 number = {2},
 pages = {273--290},
 title = {Discourse-aware Rumour stance classification in social media using sequential classifiers},
 volume = {54},
 year = {2018}
}

@article{chenimageVIRAL2021,
author = {Ling, Chen and AbuHilal, Ihab and Blackburn, Jeremy and De Cristofaro, Emiliano and Zannettou, Savvas and Stringhini, Gianluca},
title = {Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes},
year = {2021},
issue_date = {April 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {5},
number = {CSCW1},
url = {https://doi.org/10.1145/3449155},
doi = {10.1145/3449155},
journal = {Proc. ACM Hum.-Comput. Interact.},
month = {apr},
articleno = {81},
numpages = {24},
keywords = {social media, new aesthetics, internet memes, internet culture, viral content}
}

@article{Gandhi2020ScalableDO,
  title={Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images},
  author={Shreyansh Gandhi and Samrat Kokkula and Abon Chaudhuri and Alessandro Magnani and Theban Stanley and Behzad Ahmadi and Venkatesh Kandaswamy and Omer Ovenc and Shie Mannor},
  journal={2020 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2020},
  pages={2236-2245}
}

@INPROCEEDINGS{ChingHatespeechVid2020,  author={Wu, Ching Seh and Bhandary, Unnathi},  booktitle={2020 International Conference on Computational Science and Computational Intelligence (CSCI)},   title={Detection of Hate Speech in Videos Using Machine Learning},   year={2020},  volume={},  number={},  pages={585-590},  doi={10.1109/CSCI51800.2020.00104}}


@Article{wazirFoulspeech2021,
AUTHOR = {Ba Wazir, Abdulaziz Saleh and Karim, Hezerul Abdul and Abdullah, Mohd Haris Lye and AlDahoul, Nouar and Mansor, Sarina and Fauzi, Mohammad Faizal Ahmad and See, John and Naim, Ahmad Syazwan},
TITLE = {Design and Implementation of Fast Spoken Foul Language Recognition with Different End-to-End Deep Neural Network Architectures},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {3},
ARTICLE-NUMBER = {710},
URL = {https://www.mdpi.com/1424-8220/21/3/710},
PubMedID = {33494254},
ISSN = {1424-8220},
DOI = {10.3390/s21030710}
}



@inproceedings{zia-etal-2021-racist,
    title = "Racist or Sexist Meme? Classifying Memes beyond Hateful",
    author = "Zia, Haris Bin  and
      Castro, Ignacio  and
      Tyson, Gareth",
    booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.woah-1.23",
    doi = "10.18653/v1/2021.woah-1.23",
    pages = "215--219",
    abstract = "Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes",
}

@article{blmBecky2020,
author = {Apryl Williams},
title ={Black Memes Matter: \#LivingWhileBlack With Becky and Karen},
journal = {Social Media + Society},
volume = {6},
number = {4},
pages = {2056305120981047},
year = {2020},
doi = {10.1177/2056305120981047},

URL = { 
        https://doi.org/10.1177/2056305120981047
    
},
eprint = { 
        https://doi.org/10.1177/2056305120981047   
}
,
    abstract = { “BBQ Becky” and “Karen” memes reference real-world incidents in which Black individuals were harassed by White women in public spaces. In what I term the BBQ Becky meme genre, Black meme creators use humor, satire, and strategic positioning to perform a set of interrelated social commentaries on the behavior of White women. By conducting a visual Critical Technocultural Discourse Analysis (CTDA) of BBQ Becky memes, I argue that Becky and Karen memes are a cultural critique of White surveillance and White racial dominance. I find that memes in the BBQ Becky meme genre call attention to, and reject, White women’s surveillance and regulation of Black bodies in public spaces—making an important connection between racialized surveillance of the past and contemporary acts of “casual” racism. This meme genre also disrupts White supremacist logics and performative racial ignorance by framing Karens and Beckys as racist—not just disgruntled or entitled. Finally, in a subversion and reversal of power dynamics, Karen and BBQ Becky memes police White supremacy and explicitly call for consequences, providing Black communities with a form of agency. Hence, I conclude that Black memes matter in the struggle for racial equity. }
}



@inproceedings{Yoon2016WhyII,
  title={Why is it not Just a Joke? Analysis of Internet Memes Associated with Racism and Hidden Ideology of Colorblindness},
  author={Injeong Yoon},
  year={2016}
}


@article{AOMD2021,
  author    = {Lanyu Shang and
               Yang Zhang and
               Yuheng Zha and
               Yingxi Chen and
               Christina Youn and
               Dong Wang},
  title     = {{AOMD:} An Analogy-aware Approach to Offensive Meme Detection on Social
               Media},
  journal   = {CoRR},
  volume    = {abs/2106.11229},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.11229},
  eprinttype = {arXiv},
  eprint    = {2106.11229},
  timestamp = {Tue, 10 Aug 2021 11:53:23 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-11229.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@INPROCEEDINGS{9582340,  author={Shang, Lanyu and Youn, Christina and Zha, Yuheng and Zhang, Yang and Wang, Dong},  booktitle={2021 IEEE 17th International ConferenchatePix2020e on eScience (eScience)},   title={KnowMeme: A Knowledge-enriched Graph Neural Network Solution to Offensive Meme Detection},   year={2021},  volume={},  number={},  pages={186-195},  doi={10.1109/eScience51609.2021.00029}}


@article{hatePix2020,
  author    = {Benet Oriol Sabat and
               Cristian Canton{-}Ferrer and
               Xavier Gir{\'{o}}{-}i{-}Nieto},
  title     = {Hate Speech in Pixels: Detection of Offensive Memes towards Automatic
               Moderation},
  journal   = {CoRR},
  volume    = {abs/1910.02334},
  year      = {2019},
  url       = {http://arxiv.org/abs/1910.02334},
  eprinttype = {arXiv},
  eprint    = {1910.02334},
  timestamp = {Thu, 30 Apr 2020 17:36:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-02334.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@INPROCEEDINGS{9402406,  author={Giri, Roushan Kumar and Gupta, Subhash Chandra and Gupta, Umesh Kumar},  booktitle={2021 International Conference on Computer Communication and Informatics (ICCCI)},   title={An approach to detect offence in Memes using Natural Language Processing(NLP) and Deep learning},   year={2021},  volume={},  number={},  pages={1-5},  doi={10.1109/ICCCI50826.2021.9402406}}


@inproceedings{suryawanshi-etal-2020-multimodal,
    title = "Multimodal Meme Dataset ({M}ulti{OFF}) for Identifying Offensive Content in Image and Text",
    author = "Suryawanshi, Shardul  and
      Chakravarthi, Bharathi Raja  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2020.trac-1.6",
    pages = "32--41",
    abstract = "A meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Hate speech, offensive content and aggression content detection have been extensively explored in a single modality such as text or image. However, combining two modalities to detect offensive content is still a developing area. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. Therefore, it is necessary to combine both modalities to identify whether a given meme is offensive or not. Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U.S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset. We subsequently developed a classifier for this task using the MultiOFF dataset. We use an early fusion technique to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness. Our results show improvements in terms of Precision, Recall, and F-Score. The code and dataset for this paper is published in \textit{ \url{https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text} }",
    language = "English",
    ISBN = "979-10-95546-56-6",
}


@article{studamerhumor2019,
 ISSN = {0095280X, 23339934},
 URL = {https://www.jstor.org/stable/10.5325/studamerhumor.5.1.0031},
 abstract = {ABSTRACT: In the past decade, people associated with what is known as the alt-right have employed a strategy similar to that of progressive, antiracist satirists to advance a decidedly white supremacist, anti-Semitic, misogynist, and deadly serious agenda. As this article documents, the alt-right weaponizes irony to attract and radicalize potential supporters, challenge progressive ideologies and institutions, redpill normies, and create a toxic counterpublic. Discussing examples of satiric irony generated by the extreme right alongside those produced by the (often mainstream) left, this article pairs two satirical memes, two activists’ use of irony, two ambiguously satirical tweets, and two recent controversies pertaining to racism and satire so as to illustrate how people with very different political commitments employ a similar style with potent effects. Of particular significance are reverse racism discourses, including “white genocide,” and the increasingly complicated relationship between intentions, extremism, and satire.},
 author = {Viveca S. Greene},
 journal = {Studies in American Humor},
 number = {1},
 pages = {31--69},
 publisher = {[American Humor Studies Association, Penn State University Press]},
 title = {“Deplorable” Satire: Alt-Right Memes, White Genocide Tweets, and Redpilling Normies},
 volume = {5},
 year = {2019}
}









@article{nordinNeoNaziAltright2021,
author = {Tina Askanius},
title ={On Frogs, Monkeys, and Execution Memes: Exploring the Humor-Hate Nexus at the Intersection of Neo-Nazi and Alt-Right Movements in Sweden},
journal = {Television \& New Media},
volume = {22},
number = {2},
pages = {147-165},
year = {2021},
doi = {10.1177/1527476420982234},

URL = { 
        https://doi.org/10.1177/1527476420982234
    
},
eprint = { 
        https://doi.org/10.1177/1527476420982234
    
}
,
    abstract = { This article is based on a case study of the online media practices of the militant neo-Nazi organization the Nordic Resistance Movement, currently the biggest and most active extreme-right actor in Scandinavia. I trace a recent turn to humor, irony, and ambiguity in their online communication and the increasing adaptation of stylistic strategies and visual aesthetics of the Alt-Right inspired by online communities such as 4chan, 8chan, Reddit, and Imgur. Drawing on a visual content analysis of memes (N = 634) created and circulated by the organization, the analysis explores the place of humor, irony, and ambiguity across these cultural expressions of neo-Nazism and how ideas, symbols, and layers of meaning travel back and forth between neo-Nazi and Alt-right groups within Sweden today. }
}



@inproceedings{Dafaure2020TheM,
  title={The “Great Meme War:” the Alt-Right and its Multifarious Enemies},
  author={Maxime Dafaure},
  year={2020}
}

@article{proudboys2018,
author = {Julia R. DeCook},
title = {Memes and symbolic violence: \#proudboys and the use of memes for propaganda and the construction of collective identity},
journal = {Learning, Media and Technology},
volume = {43},
number = {4},
pages = {485-504},
year  = {2018},
publisher = {Routledge},
doi = {10.1080/17439884.2018.1544149},

URL = { 
        https://doi.org/10.1080/17439884.2018.1544149
    
},
eprint = { 
        https://doi.org/10.1080/17439884.2018.1544149
    
}

}



@inproceedings{mittos2019and,
  title={{``And we will fight for our race!" A} measurement study of genetic testing conversations on reddit and 4chan},
  author={Mittos, Alexandros and Zannettou, Savvas and Blackburn, Jeremy and De Cristofaro, Emiliano},
  booktitle={ICWSM},
  volume={14},
  pages={452--463},
  year={2020}
}



@article{Jordan_multinationalmeme,
author = {Jordan McSwiney and Michael Vaughan and Annett Heft and Matthias Hoffmann},
title = {Sharing the hate? Memes and transnationality in the far right’s digital visual culture},
journal = {Information, Communication \& Society},
volume = {24},
number = {16},
pages = {2502-2521},
year  = {2021},
publisher = {Routledge},
doi = {10.1080/1369118X.2021.1961006},

URL = { 
        https://doi.org/10.1080/1369118X.2021.1961006
    
},
eprint = { 
        https://doi.org/10.1080/1369118X.2021.1961006
    
}

}




@article{askaniusMurder2021,
author = {Tina Askanius and Nadine Keller},
title = {Murder fantasies in memes: fascist aesthetics of death threats and the banalization of white supremacist violence},
journal = {Information, Communication \& Society},
volume = {24},
number = {16},
pages = {2522-2539},
year  = {2021},
publisher = {Routledge},
doi = {10.1080/1369118X.2021.1974517},

URL = { 
        https://doi.org/10.1080/1369118X.2021.1974517
    
},
eprint = { 
        https://doi.org/10.1080/1369118X.2021.1974517
    
}

}


@misc{zhu2020enhance,
      title={Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution}, 
      author={Ron Zhu},
      year={2020},
      eprint={2012.08290},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Nikolas2016,
author = {Nikolas Dickerson},
title ={Constructing the Digitalized Sporting Body: Black and White Masculinity in NBA/NHL Internet Memes},
journal = {Communication \& Sport},
volume = {4},
number = {3},
pages = {303-330},
year = {2016},
doi = {10.1177/2167479515584045},

URL = { 
        https://doi.org/10.1177/2167479515584045
    
},
eprint = { 
        https://doi.org/10.1177/2167479515584045
    
}
,
    abstract = { In this article, I examine the ways sport fans construct and circulate discourses of race and masculinity in cyberspace. I do this through an examination of a set of Internet memes that juxtapose the bodies of National Hockey League players with National Basketball Association players in one single image. I argue these memes celebrate White masculinity, while at the same time constructing African American athletes as individualistic, selfish, and unwilling to sacrifice their bodies for the greater good of the team. More so, I argue that these memes construct a form of racial ideology that is representative of White backlash politics. }
}


@article{WILLIAMS2016424,
title = {Racial microaggressions and perceptions of Internet memes},
journal = {Computers in Human Behavior},
volume = {63},
pages = {424-432},
year = {2016},
issn = {0747-5632},
doi = {https://doi.org/10.1016/j.chb.2016.05.067},
url = {https://www.sciencedirect.com/science/article/pii/S0747563216303946},
author = {Amanda Williams and Clio Oliver and Katherine Aumer and Chanel Meyers},
keywords = {Memes, Internet, Racial microaggressions, Prejudice, Discrimination},
abstract = {Although more blatant forms of discrimination have declined, racial prejudice continues to manifest itself in subtle ways. For example, People of Color experience racial microaggressions (i.e., subtle slights or ‘put downs’) in their face-to-face interactions (Nadal, 2011) and in online contexts (Clark et al., 2011). This study investigates whether experiencing subtle racial discrimination offline can influence perceptions of online content, specifically racial themed Internet memes. Results indicate that although both People of Color and Whites viewed racial themed memes to be more offensive than non-racial themed memes (control images), for People of Color the ratings of racial themed memes were predicted by previous discrimination; those who reported experiencing more racial microaggressions in everyday settings rated racial themed memes as more offensive. The same pattern of results did not emerge for ratings of non-racial themed memes or for White participants. These results provide initial evidence that experiencing racial microaggressions in offline interactions may lead individuals from racial minority groups to be more likely to perceive racial discrimination in online settings.}
}

@article{NatourAboriginals2020,
author = {Ryan Al-Natour},
title ={The digital racist fellowship behind the anti-Aboriginal internet memes},
journal = {Journal of Sociology},
volume = {57},
number = {4},
pages = {780-805},
year = {2021},
doi = {10.1177/1440783320964536},

URL = { 
        https://doi.org/10.1177/1440783320964536
    
},
eprint = { 
        https://doi.org/10.1177/1440783320964536
    
}
,
    abstract = { In Australian social media platforms, non-Indigenous social media users commonly utilize digital spaces to communicate anti-Aboriginal racism. This article investigates the phenomenon of anti-Aboriginal internet memes that appear across Australian social media pages and closely examines the prominent racializations evident in these memes. This article discusses 19 internet memes and how they function in the development of a racist colonial digital movement. As this article uncovers, several racializations targeting Aboriginality in relation to skin colour and several racist stereotypes are communicated in these memes. This article unpacks the vulgarity of these internet memes and the ways they sustain a racist digital bond among participants. }
}

@inproceedings{Fairchild2020ItsFB,
  title={It’s Funny Because It’s True: The Transmission of Explicit and Implicit Racism in Internet Memes},
  author={Tabitha Fairchild},
  year={2020}
}


@article{Fernandez_2020, title={’El Negro de WhatsApp’ meme, digital blackface, and racism on social media}, volume={25}, url={https://firstmonday.org/ojs/index.php/fm/article/view/10420}, DOI={10.5210/fm.v25i12.10420}, abstractNote={<p>This paper explores how structural racism encodes itself into social media. Through the examination of a popular WhatsApp meme in Spain, I show how everyday socio-technical practices on this platform perpetuate power hierarchies based on race. As a first step, the paper links “El Negro de WhatsApp” meme with the long racist tradition of commodifying black bodies in American popular culture and beyond. Second, I argue that encrypted services like WhatsApp facilitate and amplify what Picca and Feagin (2007) refer to as the “backstage” of racism. The article concludes outlining the challenges of private, encrypted services if we are to dismantle ‘platformed racism’.</p>}, number={12}, journal={First Monday}, author={Matamoros-Fernández, Ariadna}, year={2020}, month={Jan.} }


@article{raciallit2017,
 ISSN = {10828354},
 URL = {https://www.jstor.org/stable/26529237},
 author = {Keffrelyn D. Brown},
 journal = {Race, Gender \& Class},
 number = {1-2},
 pages = {81--96},
 publisher = {Jean Ait Belkhir, Race, Gender \& Class Journal},
 title = {Why We Can’t Wait: Advancing Racial Literacy and A Critical Sociocultural Knowledge of Race for Teaching and Curriculum},
 volume = {24},
 year = {2017}
}


 @misc{gray_2018, title={POWER IN THE VISUAL: EXAMINING NARRATIVES OF CONTROLLING BLACK BODIES IN CONTEMPORARY GAMING.}, url={https://go.gale.com/ps/dispBasicSearch.do?userGroupName=anon%7Eb3178a02&prodId=LitRC}, journal={Gale Literature Resource Center}, publisher={University of Texas at Austin (University of Texas Press)}, author={Gray, Kishonna L}, year={2018}, month={Jan}} 



@article{miltneremoji2021,
author = {Kate M. Miltner},
title ={“One part politics, one part technology, one part history”: Racial representation in the Unicode 7.0 emoji set},
journal = {New Media \& Society},
volume = {23},
number = {3},
pages = {515-534},
year = {2021},
doi = {10.1177/1461444819899623},

URL = { 
        https://doi.org/10.1177/1461444819899623
    
},
eprint = { 
        https://doi.org/10.1177/1461444819899623
    
}
,
    abstract = { Emoji are miniature pictographs that have taken over text messages, emails, and Tweets worldwide. Although contemporary emoji represent a variety of races, genders, and sexual orientations, the original emoji set came under fire for its racial homogeneity: minus two “ethnic” characters, the people emoji featured in Unicode 7.0 were represented as White. This article investigates the set of circumstances that gave rise to this state of affairs, and explores the implications for users of color whose full participation in the emoji phenomenon is constrained by their exclusion. This project reveals that the lack of racial representation within the emoji set is the result of colorblind racism as evidenced through two related factors: aversion to, and avoidance of, the politics of technical systems and a refusal to recognize that the racial homogeneity of the original emoji set was problematic in the first place. }
}


@article{Gregory2019,
author = {Zobel, Gregory},
title = {Review of "Algorithms of Oppression: How Search Engines Reinforce Racism," by Noble, S. U. (2018). New York, New York: NYU Press.},
year = {2019},
issue_date = {September 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {7},
number = {3},
url = {https://doi.org/10.1145/3321388.3321392},
doi = {10.1145/3321388.3321392},
abstract = {Read and considered thoughtfully, Safiya Umoja Noble's Algorithms of Oppression: How Search Engines Reinforce Racism is devastating. It reduces to rubble the notion that technology is neutral and ideology-free. Noble's crushing the neutrality myth does several things. First, this act lays foundations for her argument: only if you recognize and understand that technology is built with, and integrates, bias, can you then be open to her primary thesis: search engines advance discriminatory and often racist content. Second, it banishes a convenient response for many self-identified meritocratic Silicon Valley "winners" and their supporters. Post-reading, some individuals may retain their beliefs in a neutral and ideology-free technology in spite of the overwhelming evidence and citations Noble brings to bear. Effective countering of Noble's claims is unlikely to occur. For professionals working in technology, information, argumentation, and/or rhetorical studies, Algorithms of Oppression is refreshing. Agonistic towards structural racism and its defenses, single-minded in its evidentiary presentation, collaborative in its acknowledgement of others' scholarship and research, Noble models many academic, critical, and social moves. Technology scholars and writers will find in Algorithms of Oppression a masterful mentor text on how to be an activist researcher scholar. Noble also makes this enjoyable reading. It is uncommon to find academic books that can simultaneously be read, used, and applied by academics and non-academics alike.},
journal = {Commun. Des. Q. Rev},
month = {nov},
pages = {30–31},
numpages = {2}
}

@inproceedings{Chandra_Jew2021,
author = {Chandra, Mohit and Pailla, Dheeraj and Bhatia, Himanshu and Sanchawala, Aadilmehdi and Gupta, Manish and Shrivastava, Manish and Kumaraguru, Ponnurangam},
title = {“Subverting the Jewtocracy”: Online Antisemitism Detection Using Multimodal Deep Learning},
year = {2021},
isbn = {9781450383301},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3447535.3462502},
doi = {10.1145/3447535.3462502},
abstract = { The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.},
booktitle = {13th ACM Web Science Conference 2021},
pages = {148–157},
numpages = {10},
keywords = {deep learning, hate speech, antisemitism, multimodal classification},
location = {Virtual Event, United Kingdom},
series = {WebSci '21}
}

@article{tuters_para2020,
author = {Marc Tuters and Sal Hagen},
title ={(((They))) rule: Memetic antagonism and nebulous othering on 4chan},
journal = {New Media \& Society},
volume = {22},
number = {12},
pages = {2218-2237},
year = {2020},
doi = {10.1177/1461444819888746},

URL = { 
        https://doi.org/10.1177/1461444819888746
    
},
eprint = { 
        https://doi.org/10.1177/1461444819888746
    
}
,
    abstract = { Previously theorised as vehicles for expressing progressive dissent, this article considers how political memes have become entangled in the recent reactionary turn of Web subcultures. Drawing on Chantal Mouffe’s work on political affect, this article examines how online anonymous communities use memetic literacy, memetic abstraction, and memetic antagonism to constitute themselves as political collectives. Specifically, it focuses on how the subcultural and highly reactionary milieu of 4chan’s /pol/ board does so through an anti-Semitic meme called triple parentheses. In aggregating the contents of this peculiar meme from a large dataset of /pol/ comments, the article finds that /pol/ users, or anons, tend to use the meme to formulate a nebulous out-group resonant with populist demagoguery. }
}


@article{zannetou2018,
  author    = {Joel Finkelstein and
               Savvas Zannettou and
               Barry Bradlyn and
               Jeremy Blackburn},
  title     = {A Quantitative Approach to Understanding Online Antisemitism},
  journal   = {CoRR},
  volume    = {abs/1809.01644},
  year      = {2018},
  url       = {http://arxiv.org/abs/1809.01644},
  eprinttype = {arXiv},
  eprint    = {1809.01644},
  timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1809-01644.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{france_mys2021,
  author    = {Francesca Gasparini and
               Giulia Rizzi and
               Aurora Saibene and
               Elisabetta Fersini},
  title     = {Benchmark dataset of memes with text transcriptions for automatic
               detection of multi-modal misogynistic content},
  journal   = {CoRR},
  volume    = {abs/2106.08409},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.08409},
  eprinttype = {arXiv},
  eprint    = {2106.08409},
  timestamp = {Thu, 14 Oct 2021 09:17:37 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08409.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{issac2018reinforcement,
  title={Reinforcement of Sexism Through Memes},
  author={Issac, Aalen Chacko}
}

@article{Siddiqi2018,
author = {Siddiqi, Nasrina and Bains, Anjuman and Aleem, Sheema and Aleem, Sheema},
year = {2018},
month = {08},
pages = {356},
title = {ANALYSING THREADS OF SEXISM IN NEW AGE HUMOUR: A CONTENT ANALYSIS OF INTERNET MEMES},
volume = {59},
journal = {Indian journal of social research}
}

@article{Drakett2018,
author = {Jessica Drakett and Bridgette Rickett and Katy Day and Kate Milnes},
title ={Old jokes, new media – Online sexism and constructions of gender in Internet memes},
journal = {Feminism \& Psychology},
volume = {28},
number = {1},
pages = {109-127},
year = {2018},
doi = {10.1177/0959353517727560},

URL = { 
        https://doi.org/10.1177/0959353517727560
    
},
eprint = { 
        https://doi.org/10.1177/0959353517727560
    
}
,
    abstract = { The Internet is a space where the harassment of women and marginalised groups online has attracted the attention of both academic and popular press. Feminist research has found that instances of online sexism and harassment are often reframed as “acceptable” by constructing them as a form of humour. Following this earlier research, this present paper explores a uniquely technologically-bound type of humour by adopting a feminist, social-constructionist approach to examine the content of popular Internet memes. Using thematic analysis on a sample of 240 image macro Internet memes (those featuring an image with a text caption overlaid), we identified two broad, overarching themes – Technological Privilege and Others. Within the analysis presented here, complex and troubling constructions of gendered identity in online humour are explored, illustrating the potential for the othering and exclusion of women through humour in technological spaces. We argue that this new iteration of heteronormative, hegemonic masculinity in online sexism, couched in “irony” and “joking”, serves to police, regulate and create rightful occupants and owners of such spaces. }
}

@InCollection{sep-perception-problem,
	author       =	{Crane, Tim and French, Craig},
	title        =	{{The Problem of Perception}},
	booktitle    =	{The {Stanford} Encyclopedia of Philosophy},
	editor       =	{Edward N. Zalta},
	howpublished =	{\url{https://plato.stanford.edu/archives/fall2021/entries/perception-problem/}},
	year         =	{2021},
	edition      =	{{F}all 2021},
	publisher    =	{Metaphysics Research Lab, Stanford University}
}

@inproceedings{kirk-etal-2021-memes,
    title = "Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset",
    author = "Kirk, Hannah  and
      Jun, Yennie  and
      Rauba, Paulius  and
      Wachtel, Gal  and
      Li, Ruining  and
      Bai, Xingjian  and
      Broestl, Noah  and
      Doff-Sotta, Martin  and
      Shtedritski, Aleksandar  and
      Asano, Yuki M",
    booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.woah-1.4",
    doi = "10.18653/v1/2021.woah-1.4",
    pages = "26--35",
    abstract = "Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synthetic examples generalize to {`}memes in the wild{'}. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that {`}memes in the wild{'} differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than {`}traditional memes{'}, including screenshots of conversations or text on a plain background. This paper thus serves as a reality-check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.",
}

@inbook{Lee2021Disen,
author = {Lee, Roy Ka-Wei and Cao, Rui and Fan, Ziqing and Jiang, Jing and Chong, Wen-Haw},
title = {Disentangling Hate in Online Memes},
year = {2021},
isbn = {9781450386517},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3474085.3475625},
abstract = {Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available hateful and offensive memes datasets. Our experiment results show that DisMultiHate is able to outperform state-of-the-art unimodal and multimodal baselines in the hateful meme classification task. Empirical case studies were also conducted to demonstrate DisMultiHate's ability to disentangle target entities in memes and ultimately showcase DisMultiHate's explainability of the multimodal hateful content classification task.},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {5138–5147},
numpages = {10}
}

@inproceedings{tian-etal-2021-mind,
    title = "{M}in{D} at {S}em{E}val-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion",
    author = "Tian, Junfeng  and
      Gui, Min  and
      Li, Chenliang  and
      Yan, Ming  and
      Xiao, Wenming",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.semeval-1.150",
    doi = "10.18653/v1/2021.semeval-1.150",
    pages = "1082--1087",
    abstract = "We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques given textual content, and the goal of subtask3 is to detect them given both textual and visual content. For subtask1, we investigate transfer learning based on pre-trained language models (PLMs) such as BERT, RoBERTa to solve data sparsity problems. For subtask3, we extract heterogeneous visual representations (i.e., face features, OCR features, and multimodal representations) and explore various multimodal fusion strategies to combine the textual and visual representations. The official evaluation shows our ensemble model ranks 1st for subtask1 and 2nd for subtask3.",
}

@inproceedings{gupta-etal-2021-volta,
    title = "{V}olta at {S}em{E}val-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble",
    author = "Gupta, Kshitij  and
      Gautam, Devansh  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.semeval-1.149",
    doi = "10.18653/v1/2021.semeval-1.149",
    pages = "1075--1081",
    abstract = "Memes are one of the most popular types of content used to spread information online. They can influence a large number of people through rhetorical and psychological techniques. The task, Detection of Persuasion Techniques in Texts and Images, is to detect these persuasive techniques in memes. It consists of three subtasks: (A) Multi-label classification using textual content, (B) Multi-label classification and span identification using textual content, and (C) Multi-label classification using visual and textual content. In this paper, we propose a transfer learning approach to fine-tune BERT-based models in different modalities. We also explore the effectiveness of ensembles of models trained in different modalities. We achieve an F1-score of 57.0, 48.2, and 52.1 in the corresponding subtasks.",
}

@inproceedings{feng-etal-2021-alpha,
    title = "Alpha at {S}em{E}val-2021 Task 6: Transformer Based Propaganda Classification",
    author = "Feng, Zhida  and
      Tang, Jiji  and
      Liu, Jiaxiang  and
      Yin, Weichong  and
      Feng, Shikun  and
      Sun, Yu  and
      Chen, Li",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.semeval-1.8",
    doi = "10.18653/v1/2021.semeval-1.8",
    pages = "99--104",
    abstract = "This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes. In this paper, we propose to use transformer based architecture to fuse the clues from both image and text. We explore two branches of techniques including fine-tuning the text pretrained transformer with extended visual features, and fine-tuning the multimodal pretrained transformers. For the visual features, we have tested both grid features based on ResNet and salient region features from pretrained object detector. Among the pretrained multimodal transformers, we choose ERNIE-ViL, a two-steam cross-attended transformers pretrained on large scale image-caption aligned data. Fine-tuing ERNIE-ViL for our task produce a better performance due to general joint multimodal representation for text and image learned by ERNIE-ViL. Besides, as the distribution of the classification labels is very unbalanced, we also make a further attempt on the loss function and the experiment result shows that focal loss would perform better than cross entropy loss. Last we have won first for subtask C in the final competition.",
}

@article{Reis_Melo_Garimella_Almeida_Eckles_Benevenuto_2020, title={A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections}, volume={14}, url={https://ojs.aaai.org/index.php/ICWSM/article/view/7356}, abstractNote={<p>Recently, messaging applications, such as WhatsApp, have been reportedly abused by misinformation campaigns, especially in Brazil and India. A notable form of abuse in WhatsApp relies on several manipulated images and memes containing all kinds of fake stories. In this work, we performed an extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites. This paper opens a novel dataset to the research community containing fact-checked fake images shared through WhatsApp for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections.</p>}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Reis, Julio C. S. and Melo, Philipe and Garimella, Kiran and Almeida, Jussara M. and Eckles, Dean and Benevenuto, Fabrício}, year={2020}, month={May}, pages={903-908} }

@article{MultimodalSent2018,
   title={Multimodal Sentiment Analysis To Explore the Structure of Emotions},
   url={http://dx.doi.org/10.1145/3219819.3219853},
   DOI={10.1145/3219819.3219853},
   journal={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
   publisher={ACM},
   author={Hu, Anthony and Flaxman, Seth},
   year={2018},
   month={Jul} }
   
   
@misc{duong2017multimodal,
      title={Multimodal Classification for Analysing Social Media}, 
      author={Chi Thang Duong and Remi Lebret and Karl Aberer},
      year={2017},
      eprint={1708.02099},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}