2020/04/08 by Serra Sinem Tekiroğlu, Serra Sinem Tekiroglu, Yi-Ling Chung +4 · 8 citations
Computer Science · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Social and Information Networks (cs.SI) #cs.CL #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.2004.04216
To appear at ACL 2020 (long paper)
arxiv created 2020/04/08 · openalex publication_date 2020/04/08 · arxiv updated 2020/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.