Hate Speech and Offensive Language Detection in Bengali

Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 286–296 November 20–23, 2022. ©2022 Association for Computational Linguistics 286 Hate Speech and Offensive Language Detection in Bengali Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee Indian Institute of Technology Kharagpur, West Bengal, India mithundas@iitkgp.ac.in, som.iitkgpcse@kgpian.iitkgp.ac.in, punyajoys@iitkgp.ac.in, animeshm@cse.iitkgp.ac.in Abstract Social media often serves as a breeding ground for various hateful and offensive content. Iden- tifying such content on social media is crucial due to its impact on the race, gender, or re- ligion in an unprejudiced society. However, while there is extensive research in hate speech detection in English, there is a gap in hateful content detection in low-resource languages like Bengali. Besides, a current trend on so- cial media is the use of Romanized Bengali for regular interactions. To overcome the ex- isting research’s limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Roman- ized Bengali tweets. We implement several baseline models for the classification of such hateful posts. We further explore the interlin- gual transfer mechanism to boost classification performance. Finally, we perform an in-depth error analysis by looking into the misclassified posts by the models. While training actual and Romanized datasets separately, we observe that XLM-Roberta performs the best. Further, we witness that on joint training and few-shot train- ing, MuRIL outperforms other models by in- terpreting the semantic expressions better. We make our code and dataset public for others1. 1 Introduction Social media websites like Twitter and Facebook have brought billions of people together and given them the opportunity to share their thoughts and opinions rapidly. On the one hand, it has facilitated communication and the growth of social networks; on the other, it has been exploited to propagate mis- information, violence, and hate speech (Mathew et al., 2019; Das et al., 2020) against users based on their gender, race, religion, or other character- istics. If such content is left unaddressed, it may result in widespread conflict and violence, raising 1https://github.com/hate-alert/ Bengali_Hate concerns about the safety of human rights, the rule of law, and freedom of speech, all of which are cru- cial for the growth of an unprejudiced democratic society (Rizwan et al., 2020). Organizations such as Facebook have been blamed for being a forum for instigating anti-Muslim violence in Sri Lanka that resulted in the deaths of three individuals2, and a UN report accused them of disseminating hate speech in a way that contributed significantly to the plausible genocide of the Rohingya population in Myanmar3. In order to reduce the dissemination of such harmful content, these platforms have developed certain guidelines4 that the users of these platforms ought to comply with. If these rules aren’t followed, the post can get deleted, or the user’s account might get suspended. Even to diminish the harmful con- tent from their forum, these platforms engage mod- erators (Newton, …

Content truncated to 500 words