KDD for a Safer Online Social Space

On January 26, 2022, SIGKDD Singapore held an online lunch-time seminar featuring two talks on KDD for a Safer Online Social Space by two experts in the field.

Talk#1: Perils and Promises of Automated Hate Speech Detection

Abstract: Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. This talk aims to introduce the pressing problem of online hate speeches and discuss the automated hate speech detection methods. Specifically, we will discuss the various NLP approaches for hate speech detection and highlight the potential future research directions such as multimodal and multilingual hate speech detection.

Bios: Roy is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design. He is a faculty of the transformative Design and Artificial Intelligence program. His research lies in the intersection of data mining, machine learning, social computing, and natural language processing. He is leading the Social AI Studio, a research group that focuses on designing the next-generation social artificial intelligence systems. He has published in top-tier venues in data mining and computation linguistics domains. He serves in the program committees of multiple top conferences. He is currently part of the editorial board for the Social Network Analysis and Mining journal.

Talk#2: Rumor Detection with Generative Adversarial Learning

Abstract: Online rumors can cause devastating outcomes to individuals and society. Analysis shows that the widespread of rumors typically results from deliberate promotion of uncredited information aiming to shape the public opinions. On the other hand, fact-checking currently follows investigative journalism requiring significant amount of time and manual effort, which cannot keep the pace of generation of various rumors on a daily basis. In this talk, I will introduce automatic approaches, techniques and new development for combating online rumors in social media from the perspective of natural language processing and adversarial learning. I will also discuss the characterization of online rumors including their linguistic, temporal and propagational features and dynamics, together with some takeaways from past and ongoing research.

Bio: Wei Gao is currently an Assistant Professor of Computer Science at Singapore Management University. His research generally interests natural language processing, information retrieval, artificial intelligence and social computing. Currently he has been working on the topic of rumor detection and computational fact checking. His publications appear in the major international venues including ACL, AAAI/IJCAI, SIGIR, WSDM, WWW, ACM TOIS, IEEE TKDE, etc. He broadly serves the top conferences and leading journals in his relevant field. He is an Associate Editor of ACM TALLIP and a member of standing review committees of Transactions of the ACL and Computational Linguistics Journal.

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