1. 主讲人介绍
Dr. Yan Wang is currently a Full Professor in the School of Computing, Macquarie University, Australia. He is also the Research Director of Macquarie University Centre for Frontier AI Research (FAIR). He received his PhD from Harbin Institute of Technology (HIT), P. R. China. Prior to joining Macquarie University in 2003, he was a Postdoctoral Fellow/Research Fellow in the Department of Computer Science, School of Computing, National University of Singapore (NUS). He has published a number of research papers in international conferences including AAAI, AAMAS, CVPR, ICDE, IJCAI, KDD, NeurIPS, SIGIR, WWW, and journals including CSUR, TIST, TKDD, TKDE, TOIS, TSC and TWEB. In addition, the proposed solution on dual-target cross-domain recommendation systems has been adopted by Alipay system. His research interests cover recommender systems, fake news detection/mitigation, data analytics, trust management and social computing.
2. 讲座介绍
Title: Fake News Mitigation and Detection
Abstract: Fake news mitigation and detection are challenging tasks. This talk will introduce some recent research work in this area and share the discussion behind the work. The first part is about fake news mitigation. Firstly, this talk will introduce a novel solution, which is the first in the literature to leverage recommender system technique for fake news mitigation. The proposed solution can differentiate the events behind news, identify the veracity of news, and recommend true news to users based on their historical data. Secondly, this talk will introduce a novel solution for unbiased and true news recommendation. It can not only capture users’ high- and low-level interests, enhancing next-news recommendation accuracy, but also effectively separate polarity and veracity information from news contents and model them more specifically, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations.
The second part is about fake news detection. Firstly, this talk will introduce a novel work on how to analyse user-news engagement for cross-domain fake news detection. The user-news engagement includes user reposting behaviors and comments in different domains. Secondly, this talk will introduce a novel work on cross-domain fake news detection. This work first analyses both news content and user-news engagement for cross-domain knowledge transfer from a macro perspective. Moreover, from a micro perspective, this work disentangles veracity-relevant and veracity-irrelevant features before extracting domain-shared and domain-specific features.
