A BERT-based Hate Speech Classifier from Transcribed Online Short-Form Videos

Abstract

With the rise of human-centric technologies such as social media platforms, the amount of hate also continues to grow proportionally with the increasing number of users worldwide. TikTok is one of the most-used social media platforms due to its feature that allows users to express themselves via creating and sharing short-form videos based on any desired topic and content. In addition, it has also become a platform for political discourse and mudslinging as users can freely express an opinion and indirectly debate with random people online. In this study, we propose the use of BERT, a complex bidirectional transformer-based model, for the task of automatic hate speech detection from speech transcribed from Tagalog TikTok videos. Results of our experiments show that a BERT-based hate speech classifier scores 61% F1. We also extended the task beyond several algorithms such as LSTM, Naïve Bayes, and Decision Tree and found out that traditional methods such as a simple Bernoulli Naïve Bayes approach remain at par with the BERT model.