Explainable Detection of Fake News and Cyberbullying on Social Media

Cheng-Te Li

Institute of Data Science, National Cheng Kung University


Prof. Li is now an Associate Professor at Institute of Data Science, National Cheng Kung University (NCKU), Tainan, Taiwan. He received his Ph.D. degree from Graduate Institute of Networking and Multimedia, National Taiwan University. Before joining NCKU, he was an Assistant Research Fellow at CITI, Academia Sinica. Prof. Li's research interests include Machine Learning and Data Mining, Natural Language Processing, Social Network Analysis, and Recommender Systems. He had published a series of papers in top conferences, including, KDD, TheWebConf, SIGIR, CIKM, ACL, IJCAI, and ACM Multimedia. Prof. LI's academic recognitions include: 2019 K. T. Li Young Researcher Award, 2018 MOST Young Scholar Fellowship (The Columbus Program), 2016 Exploration Research Award of Pan Wen Yuan Foundation, 2012 Facebook Fellowship, and 2010 Microsoft Research Asia Fellowship.


Social media ubiquitously penetrates into people's daily life and allows interactions between people. User-generated text data not only enables novel applications, but also provides user digital footprints for us to analyze a variety of human behaviors. In this talk, we will share two of our recent studies on combating anti-social behaviors: detecting fake news and identifying cyberbullying behaviors on social media. We will reveal three important insights. First, it is possible to predict anti-social behaviors without social network information. Second, graph neural networks (GNN) is effective in improving the performance of such two tasks. Third, our models can provide model explainability to understand the language use of anti-social behaviors. In the end of this talk, we will point out future directions on fighting with fake news and cyberbullying in social media.

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