{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semeval-2019-task-6-identifying-and-1","title":"SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)","arxiv_id":"1903.08983","date":"2019-03-19","proceeding":"SEMEVAL 2019 6","authors":["Marcos Zampieri","Shervin Malmasi","Preslav Nakov","Sara Rosenthal","Noura Farra","Ritesh Kumar"],"abstract":"We present the results and the main findings of SemEval-2019 Task 6 on\nIdentifying and Categorizing Offensive Language in Social Media (OffensEval).\nThe task was based on a new dataset, the Offensive Language Identification\nDataset (OLID), which contains over 14,000 English tweets. It featured three\nsub-tasks. In sub-task A, the goal was to discriminate between offensive and\nnon-offensive posts. In sub-task B, the focus was on the type of offensive\ncontent in the post. Finally, in sub-task C, systems had to detect the target\nof the offensive posts. OffensEval attracted a large number of participants and\nit was one of the most popular tasks in SemEval-2019. In total, about 800 teams\nsigned up to participate in the task, and 115 of them submitted results, which\nwe present and analyze in this report.","url_abs":"http://arxiv.org/abs/1903.08983v3","url_pdf":"http://arxiv.org/pdf/1903.08983v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semeval-2019-task-6-identifying-and-1","repo_url":"https://github.com/VadymV/OffensEval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"semeval-2019-task-6-identifying-and-1","repo_url":"https://github.com/causalate-mitigates-bias/causal-ate-mitigates-bias","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"language-identification","task_name":"Language Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.08983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.08983"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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