• Hybrid Reciprocal Recommender Systems: Integrating Item-to-User Principles in Social Media Personalisation (James Neve and Ivan Palomares) PDF -TALK
• A hierarchical clustering algorithm for characterizing social media users (Priyanka Sinha, Lipika Dey, Pabitra Mitra and Dilys Thomas) PDF - TALK
• Mitigating Misinformation in Online Social Network with Top-k Debunkers and Evolving User Opinions (Akrati Saxena, Wynne Hsu, Mong Li Lee, Hai Leong Chieu, Lynette Ng and Loo-Nin Teow) PDF - TALK
• On Twitter Purge: A Retrospective Analysis of Suspended Users (Farhan Asif Chowdhury, Lawrence R Allen, Mohammad Yousuf and Abdullah Mueen) PDF - TALK
• Social Network Influence Ranking via Embedding Network Interactions for User Recommendation (Hongbo Bo, Ryan McConville, Jun Hong and Weiru Liu) PDF - TALK
• Boosting recommender systems with advanced embedding models (Gjorgjina Cenikj and Sonja Gievska) PDF - TALK
• Dynamic Network Modeling from Motif-Activity (Giselle Zeno, Timothy La Fond and Jennifer Neville) PDF - TALK

Abstract

While social media had ubiquitously penetrate into into people's daily life, where 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.