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

2019/03/19 by Marcos Zampieri, Zampieri, Marcos, Shervin Malmasi +10 · 13 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

paper · pdf · doi:10.48550/arxiv.1903.08983

openalex publication_date 2019/03/19 · 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\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.\n

Citations

• Towards NLP with Deep Learning: Convolutional Neural Networks and\n Recurrent Neural Networks for Offensive Language Identification in Social\n Media
• Enriching Word Vectors with Subword Information

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Related

• Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and\n Categorize Offensive Tweets
• SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
• Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets
• Identifying Offensive Posts and Targeted Offense from Twitter
• Identifying and Categorizing Offensive Language in Social Media