  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.
