Utilising Twitter metadata for hate classification

Warke, O., Jose, J. M. and Breitsohl, J. (2023) Utilising Twitter metadata for hate classification. In: Kamps, J., Goeuriot, L., Crestani, F., Maistro, M., Joho, H., Davis, B., Gurrin, C., Kruschwitz, U. and Caputo, A. (eds.) Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II. Series: Lecture notes in computer science (13981). Springer: Cham, pp. 676-684. ISBN 9783031282379 (doi: 10.1007/978-3-031-28238-6_59)

Abstract

Social media has become an essential daily feature of people’s lives. Social media platforms provide individuals wishing to cause harm with an open, anonymous, and far-reaching channel. As a result, society is experiencing a crisis concerning hate and abuse on social media. This paper aims to provide a better method of identifying these instances of hate via a custom BERT classifier which leverages readily available metadata from Twitter alongside traditional text data. With Accuracy, F1, Recall and Precision scores of 0.85, 0.75, 0.76, and 0.74, the new model presents a competitive performance compared to similar state-of-the-art models. The increased performance of models within this domain can only benefit society as they provide more effective means to combat hate on social media.

• Item Type: Book Sections
• Keywords: Hate, social media, deep learning, metadata.
• Status: Published
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Warke, Mr Oliver and Breitsohl, Dr Jan
• Authors: Warke, O., Jose, J. M., and Breitsohl, J.
• College/School: College of Science and Engineering > School of Computing Science; College of Social Sciences > Adam Smith Business School > Management
• Publisher: Springer
• ISBN: 9783031282379
• Published Online: 17 March 2023