Preventhate.org | Policyinstitute.net Algorithms Developing an online hate classifer for multiple social media platforms (Springer)

Developing an online hate classifer for multiple social media platforms (Springer)

“To address this research gap, we collect a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labeled as non-hateful and the remaining 20% labeled as hateful. We then experiment with several classifcation algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). While all the models signifcantly outperform the keyword-based baseline classifer, XGBoost using all features performs the best (F1=0.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions.”

https://d-nb.info/1208085050/34

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Post

%d bloggers like this: