Papers › K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
Jean Lee, Taejun Lim, Heejun Lee, Bogeun Jo, Yangsok Kim, HeeGeun Yoon, Soyeon Caren Han
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections