Modeling Hate Speech Detection in Social Media Interactions Using Bert

Hate speech propagation in social media sites has been happening over time and there is need to accurately identify and counter it so that those offended can seek redress and offenders can be punished for perpetrating the vice. In this paper, we demonstrate how fine tuning a pre-trained Google Bidirectional Encoder Representation from Transformers (BERT) model has been used to achieve an improvement in accuracy of classification of tweets as either hate speech or not. Random forests and logistic regression algorithms have been used to build baseline models with a publicly available twitter dataset from hatebase.org. To validate the BERT model, we collected data using Tweepy API and combined with data from hatebase.org for training. The results obtained show an improvement in accuracy of tweets classification as either hate speech or not from the baseline models by 7.22%.