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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

2019/03/19 by Marcos Zampieri, Zampieri, Marcos, Shervin Malmasi +10 · 22 citations
Computer Science · Psychology · Social Sciences · #Bullying, Victimization, and Aggression #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Swearing, Euphemism, Multilingualism #cs.CL

paper · pdf · doi:10.48550/arxiv.1903.08983

Proceedings of the International Workshop on Semantic Evaluation (SemEval)

openalex publication_date 2019/03/19 · arxiv created 2019/04/27 · arxiv updated 2019/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.

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