TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter-related Microblogs on Twitter
This work addresses hate speech identification for social media platforms, particularly in the context of the Black Lives Matter movement, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of hate speech detection by creating TweetBLM, a manually annotated dataset of 9,165 tweets related to the Black Lives Matter movement, and they analyzed various machine learning models for classification, with BERTlarge achieving the best performance (specific numbers not provided in abstract).
In the past few years, there has been a significant rise in toxic and hateful content on various social media platforms. Recently Black Lives Matter movement came into the picture, causing an avalanche of user generated responses on the internet. In this paper, we have proposed a Black Lives Matter related tweet hate speech dataset TweetBLM. Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement. We annotated the tweets into two classes, i.e., HATE and NONHATE based on their content related to racism erupted from the movement for the black community. In this work, we also generated useful statistical insights on our dataset and performed a systematic analysis of various machine learning models such as Random Forest, CNN, LSTM, BiLSTM, Fasttext, BERTbase, and BERTlarge for the classification task on our dataset. Through our work, we aim at contributing to the substantial efforts of the research community for the identification and mitigation of hate speech on the internet. The dataset is publicly available.