报告时间：2025年6月26日（星期四）15:00-17:30

报告地点：合肥工业大学管理学院925会议室

报告人：Zhepeng Li 李哲鹏

工作单位：香港大学 

举办单位：合肥工业大学管理学院

报告简介：

Hate speech is a major problem on social media platforms. Automatic hate speech detection methods relying on machine learning models, which learn from manually labeled datasets, have been proposed in both academia and industry. However, there is increasing evidence that hate speech detection datasets labeled by general annotators (e.g., amateurs or MTurk workers) contain systematic bias, as they cannot effectively consider language use differences among different speakers. When such biased datasets are used to train machine learning models, the resulting models will also be biased. Unlike general annotators, experts can produce much less biased annotations. However, expert annotations cannot be efficiently obtained in large quantity. This paper bridges the gap by adopting a weakly supervised learning method for hate speech detection using a small number of expert annotations. We propose a novel design that uses contrastive learning and prompt-based learning based on large language models, incorporating a group estimator, a pair generator, and knowledge injection. Using real-world Twitter posts written by African American English speakers and other racial groups as an example, extensive experiments were conducted to demonstrate the superior performance of the proposed method. The proposed approach was also evaluated on data in the LGBTQ+ community and achieved consistent results. The study has important academic and practical implications for hate speech detection and large language models.

报告人简介：

Dr. Zhepeng (Lionel) Li is an associate professor of Information Systems in the IIM area at the Faculty of Business and Economics, The University of Hong Kong. He received PhD degree in Operations and Information Systems from University of Utah, USA. His research focuses on computational design science, with a particular emphasis on leveraging machine learning and artificial intelligence technologies to address business and societal challenges. His work has been published in leading journals across disciplines, including Management Science, Information Systems Research, MIS Quarterly, and ACM Transactions. Dr. Li’s studies have been supported by various sources, including the General Research Fund from the University Grants Committee of Hong Kong (GRF), Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), General Program from the National Natural Science Foundation of China (NSFC), and industrial sponsors. He has received design science research award at INFORMS and participated in serving international associations and organizing events, such as INFORMS College on AI, INFORMS AI cluster, INFORMS workshop on Data Science, and Summer Workshop on AI for Business.