With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon is especially relevant in South Korea, where large-scale hate speech detection algorithms have not yet been broadly explored. In this paper, we introduce "KoMultiText", a new comprehensive, large-scale dataset collected from a well-known South Korean SNS platform. Our proposed dataset provides annotations including (1) Preferences, (2) Profanities, and (3) Nine types of Bias for the text samples, enabling multi-task learning for simultaneous classification of user-generated texts. Leveraging state-of-the-art BERT-based language models, our approach surpasses human-level accuracy across diverse classification tasks, as measured by various metrics. Beyond academic contributions, our work can provide practical solutions for real-world hate speech and bias mitigation, contributing directly to the improvement of online community health. Our work provides a robust foundation for future research aiming to improve the quality of online discourse and foster societal well-being. All source codes and datasets are publicly accessible at https://github.com/Dasol-Choi/KoMultiText.

翻译：随着在线服务的增长，对先进文本分类算法（如情感分析和偏见文本检测）的需求日益凸显。在线服务的匿名性常导致偏见性和有害语言的存在，这对维持在线社区健康构成了挑战。这一现象在韩国尤为突出，大规模仇恨言论检测算法尚未得到广泛探索。本文提出"KoMultiText"——一个从韩国知名社交网络服务平台收集的全新大规模综合数据集。该数据集为文本样本提供了包含(1)偏好性、(2)粗俗语以及(3)九种偏见类型在内的标注，支持对用户生成文本进行多任务联合分类。通过利用基于BERT的最先进语言模型，我们的方法在多种分类任务上超越了人类水平的准确率（以多种指标衡量）。除学术贡献外，我们的工作可为实际场景中的仇恨言论和偏见缓解提供实用方案，直接促进在线社区健康度的提升。本研究为未来旨在改善网络话语质量、促进社会福祉的研究提供了坚实基础。所有源代码和数据集均可通过https://github.com/Dasol-Choi/KoMultiText公开获取。