报告时间：2025年07月02日（星期三）9:00-11:30

报告地点：合肥工业大学管理学院1425会议室

报告人：方晓 教授

工作单位：美国特拉华大学

举办单位：合肥工业大学管理学院

报告简介：

An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at the early stage of an infodemic is key to manage it and reduce its harm to public health. An early stage infodemic is characterized by a large volume of unlabeled information concerning a disease. As a result, conventional misinformation detection methods are not suitable for this misinformation detection task because they rely on labeled information in the infodemic domain to train their models. To address the limitation of conventional methods, state-of-the-art methods learn their models using labeled information in other domains to detect misinformation in the infodemic domain. The efficacy of these methods depends on their ability to mitigate both covariate shift and concept shift between the infodemic domain and the domains from which they leverage labeled information. These methods focus on mitigating covariate shift but overlook concept shift, rendering them less effective for the task. In response, we theoretically show the necessity of tackling both covariate shift and concept shift as well as how to operationalize each of them. Built on the theoretical analysis, we develop a novel misinformation detection method that addresses both covariate shift and concept shift. Using two real-world datasets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over state-of-the-art misinformation detection methods as well as prevalent domain adaptation methods that can be tailored to solve the misinformation detection task.

报告人简介：

Xiao Fang is Professor of MIS at Lerner College of Business & Economics , University of Delaware. He also holds a courtesy appointment at the Department of Electrical and Computer Engineering, University of Delaware. His current research focuses on financial technology, and healthcare analytics, with methods and tools drawn from reference disciplines including Computer Science (e.g., Machine Learning) and Management Science (e.g., Optimization). He has published in business journals including Management Science, Operations Research, MIS Quarterly, and Information Systems Research as well as computer science outlets such as ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Professor Fang received the 2017 INFORMS ISS Design Science Award. He co-founded INFORMS Workshop on Data Science in 2017 and Summer Workshop on AI for Business (SWAIB) in 2024. He currently serves as a Senior Editor for MIS Quarterly.