Research Talk: Prof Yan Wang|24 July 2025

Talk title: Fake News Mitigation and Detection

Speaker: Prof Yan Wang

Speaker bio:

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.

Prof. Wang has served on the editorial board of several international journals, including IEEE Transactions on Services Computing (TSC), Service-Oriented Computing & Applications (SOCA) by Springer, and Human-centric Computing and Information Sciences (HCIS) by Springer. He also served as a General co-Chair of IEEE ATC2013, IEEE ATC2014, IEEE MS2015, IEEE ICWS2016 and IEEE CLOUD2017, a Program co-Chair of IEEE SCC2011, ATC2011, IEEE MS2014, IEEE SCC2018, IEEE SOSE2018 and IEEE SCC2019, and a Local Organisation Chair of IEEE DSAA2020.

Prof. Wang's research team has received a number of awards, including 4 Best (Student) Paper Awards from IEEE SCC2010, IEEE TrustCom2012, IEEE ICWS2016 and IEEE DSAA2024 respectively, Vice-Chancellor's (University President) Commendation for Academic Excellence in PhD thesis for 5 times (2017, 2020, 2020, 2024, 2025), and the nomination for Australasian Distinguished Doctoral Dissertation for 3 times. Prof. Wang received 2017 Outstanding Service Award from the IEEE Technical Committee on Services Computing (TC-SVC), IEEE Computer Society

Talk 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.