“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/1208085050/34Share this:FacebookXLike this:Like Loading... Post navigation An Empirical Study of Offensive Language in Online Interactions (Rochester Institute of Technology) Classification of Hate Speech Using Deep Neural Networks (HAL)