Etienne P Jacquot - 08/19/2021
This project was started in 2019 when Annenberg School for Communication faculty member Dr Lingel brought to our attention that there is interest in using machine learning tools on social media content.
*UPDATE: On 08/18/2021 Marlon identified that our AWS security & compliance score was not great. One remediation is to block public access for internet to SageMaker notebook instances. More information here: https://github.com/aquasecurity/cloud-security-remediation-guides/blob/master/en/aws/sagemaker/notebook-direct-internet-access.md. Thus I am commiting all changes to my github repo to remove all content & delete this non-compliant sagemaker notebook instance. In order to commit from git extension on SageMaker, you cannot use HTTPS & thus you need to setup a personal access token.
You need access to AWS SageMaker, either via your own account or via Pennkey ASC account https://aws.cloud.upenn.edu/.
- For this project you will need access to SageMaker & S3 bucket for your training & testing split data...
Please be sure to run your resources and shut down when not in use! Otherwise you will be charged for idle resources...
WWE SuperStar Ronda Rousey:
This example for ML model training is based on WWE SuperStar Ronda Rousey's public instagram page https://www.instagram.com/rondarousey/.
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SOCIAL MEDIA CONTENT --> Using the free service PhantomBuster to get all instagram picture URLs for various WWE users (tested for Roman Reigns & Ronda Rousey though of course there are others!)
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AI FOR IMAGE ANALYSIS --> We then pass each image to AWS Rekognition via Python-SDK to get detected objects & features with >95% confidence interval.
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SUPERVISED LEARNING --> I manually went through ~1,500 Ronda Rousey instagram images (chronological order going back in mid 2019) to code a binary value
YESorNOon whether the photograph was in KAYFABE... This CSV data is saved in directory wwe_instagram_data- This is a whole thing in WWE that I wasn't really familiar with, but basically breaking kayfabe is like breaking the 4th wall / being out of character in WWE... For example, an instagram photo of a sick headslam is IN KAYFABE but a photo of eating breakfast with your family is NOT KAYFABE
- More info here & here
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RUN CODE HERE --> Annenberg_ML_Kayfabe_Training.ipynb is a python notebook based on AWS SageMaker example for bank csv data w/ their marketplace offering XGBoost.
- More info on AWS XGBoost example here
Machine learning is an iterative step w/ hyperparameter fine tuning! Please note that this example does not really go into changing values, just follows the defaults for XGBoost...
- This will output a trained CSV data/train04222021.csv file, you can ignore this for the most part... this gets used to generate the Confusion Matrix for the training & testing data. For example, on the most recent run I got a greater than 83% true positive result on my
kayfabe-detectorfor a 70/30 row split (1029, 415) (441, 415) w/ 415 columns of Rekognition data 1/0 for >95% confidence
Overall Classification Rate: 83.9%
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Predicted----> No Kayfabe Kayfabe
Observed
No Kayfabe 85% (175) 17% (41)
Kayfabe 15% (30) 83% (195)
While I was unable to scale up for millions of WWE Instagram public userfeed images, this project was a helpful introduction to:
- AWS microservices Rekog & SageMaker
- Instagram social media web scraping & analysis
- Principles of supervised ML model training
- Pitfalls of not starting a project w/ git for versioning
- Considerations on scaling up with containers / cloud resources
This project & notebook was used as a demonstration for ASC researchers for ongoing project --> https://github.com/jmparelman/SageMaker_DNMF
))
UPDATE FROM NXCOMMJHUB - ATNJQT