2023/11/16 by Sarah Masud, Masud, Sarah, Mohammad Aflah Khan +5 · 1 citation
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Freedom of Expression and Defamation #Hate Speech and Cyberbullying Detection #Internet Traffic Analysis and Secure E-voting
paper · pdf · doi:10.48550/arxiv.2311.09834
openalex publication_date 2023/11/16 · openalex created_date 2023/11/18 · openalex updated_date 2026/07/28
As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it. Reactively, using black-box models to identify hateful content can perplex users as to why their posts were automatically flagged as hateful. On the other hand, proactive mitigation can be achieved by suggesting rephrasing before a post is made public. However, both mitigation techniques require information about which part of a post contains the hateful aspect, i.e., what spans within a text are responsible for conveying hate. Better detection of such spans can significantly reduce explicitly hateful content on the web. To further contribute to this research area, we organized HateNorm at HASOC-FIRE 2023, focusing on explicit span detection in English Tweets. A total of 12 teams participated in the competition, with the highest macro-F1 observed at 0.58.