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With the growth of online services, the need for advanced text classificationalgorithms, such as sentiment analysis and biased text detection, has becomeincreasingly evident. The anonymous nature of online services often leads tothe presence of biased and harmful language, posing challenges to maintainingthe health of online communities. This phenomenon is especially relevant inSouth Korea, where large-scale hate speech detection algorithms have not yetbeen broadly explored. In this paper, we introduce "KoMultiText", a newcomprehensive, large-scale dataset collected from a well-known South Korean SNSplatform. Our proposed dataset provides annotations including (1) Preferences,(2) Profanities, and (3) Nine types of Bias for the text samples, enablingmulti-task learning for simultaneous classification of user-generated texts.Leveraging state-of-the-art BERT-based language models, our approach surpasseshuman-level accuracy across diverse classification tasks, as measured byvarious metrics. Beyond academic contributions, our work can provide practicalsolutions for real-world hate speech and bias mitigation, contributing directlyto the improvement of online community health. Our work provides a robustfoundation for future research aiming to improve the quality of onlinediscourse and foster societal well-being. All source codes and datasets arepublicly accessible at https://github.com/Dasol-Choi/KoMultiText.

KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services