mlsanityData to 14 Oct 2026

K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment

arXiv 2208.10684 ↗First posted 23 Aug 2022cs.CL cs.AIcited by 13

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Abstract (arXiv)

Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baseline experiments on K-MHaS using Korean-BERT-based language models with six different metrics. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 23 Aug 2022.

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10 of 26 references.

References · 10

  1. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  2. 2016Ex Machina: Personal Attacks Seen at Scalecited by 210
  3. 2020Directions in Abusive Language Training Data: Garbage In, Garbage Outcited by 140
  4. 2020SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)cited by 138
  5. 2019Multilingual and Multi-Aspect Hate Speech Analysiscited by 133
  6. 2017Detecting Online Hate Speech Using Context Aware Modelscited by 72
  7. 2020BEEP! Korean Corpus of Online News Comments for Toxic Speech Detectioncited by 39
  8. 2020KR-BERT: A Small-Scale Korean-Specific Language Modelcited by 17
  9. 2021CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detectioncited by 14
  10. 2020Detect All Abuse! Toward Universal Abusive Language Detection Modelscited by 12

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Citing papers per year: 2023: 3; 2024: 5; 2025: 3; 2026: 2.

13 papers.

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  1. 2024CultureLLM: Incorporating Cultural Differences into Large Language Modelscited by 50
  2. 2023KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applicationcited by 24
  3. 2024CulturePark: Boosting Cross-cultural Understanding in Large Language Modelscited by 24
  4. 2024BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Banglacited by 10
  5. 2023K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratingscited by 5
  6. 2023KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Servicescited by 3
  7. 2024Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attackscited by 2
  8. 2024A Survey on Automatic Online Hate Speech Detection in Low-Resource Languagescited by 2
  9. 2025No Language Data Left Behind: A Comparative Study of CJK Language Datasets in the Hugging Face Ecosystemcited by 0
  10. 2025Defining, Understanding, and Detecting Online Toxicity: Challenges and Machine Learning Approachescited by 0
  11. 2025From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discoursecited by 0
  12. 2026KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacterscited by 0
1 more
  1. 2026AEGIS: Awareness-Enhanced Guidance for Iterative Safeguardcited by 0