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

arXiv 1903.08983 ↗First posted 19 Mar 2019cs.CLcited by 206

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Abstract (arXiv)

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.

Key facts. Cited by 206 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2019.

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Citing papers per year: 2021: 60; 2022: 34; 2023: 41; 2024: 39; 2025: 20; 2026: 12.

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  10. 2021XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyondcited by 114
  11. 2024GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysiscited by 87
  12. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 86
38 more
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  10. 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
  11. 2023"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social mediacited by 45
  12. 2024Language Models for Text Classification: Is In-Context Learning Enough?cited by 39
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  14. 2021Civil Rephrases Of Toxic Texts With Self-Supervised Transformerscited by 32
  15. 2021Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Gocited by 32
  16. 2021UNIREX: A Unified Learning Framework for Language Model Rationale Extractioncited by 32
  17. 2021MUDES: Multilingual Detection of Offensive Spanscited by 30
  18. 2021FBERT: A Neural Transformer for Identifying Offensive Contentcited by 30
  19. 2021Dynamic Language Models for Continuously Evolving Contentcited by 27
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  21. 2021Ruddit: Norms of Offensiveness for English Reddit Commentscited by 26
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  23. 2024A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practicecited by 26
  24. 2022APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversationscited by 23
  25. 2021Multilingual Offensive Language Identification for Low-resource Languagescited by 22
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  31. 2022Hate Speech and Offensive Language Detection in Bengalicited by 19
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  33. 2021Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languagescited by 17
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  36. 2021HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognitioncited by 14
  37. 2021An Exploratory Analysis of the Relation Between Offensive Language and Mental Healthcited by 14
  38. 2022BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contextscited by 14

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