Cyberbullying Detection with Psychological Insights and Complex Media Data

In this tech talk, we will have Lu Cheng, a fifth year phd student from Arizona State University to share her research works in cyberbullying detection using AI.

Title: Advancing Cyberbullying Detection with Psychological Insights and Complex Media Data

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Abstract:
Cyberbullying is rapidly becoming one of the most serious online risks for adolescents. This has motivated work on machine learning methods to automate the process of cyberbullying detection. Cyberbullying has so far mostly been viewed as one-off incidents reflected in single pieces of text. However, in social science and psychology, cyberbullying behavior comes with two unique attributes: repetitive patterns and power imbalance. This psychological definition suggests that to capture these cyberbullying attributes, we need to look beyond single pieces of text and leverage complex social media data that consist of multiple modalities such as images/videos, a sequence of comments, user information, spatial location, and other social content. In this talk, I will first introduce a novel task of social media session-based cyberbullying detection and its new challenges/opportunities. I will then discuss how to address some of these challenges guided by the psychological findings and with advanced machine learning algorithms. Finally, I will touch upon the remaining core challenges to help facilitate future interdisciplinary research efforts.

Speaker Bio: Lu Cheng is a fifth-year Ph.D. candidate in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). Advised by Prof. Huan Liu, Lu's research focuses on bridging from conceptual AI principles to responsible AI practice using both statistical and causality-aware methods. Lu's work has appeared in and been invited to top venues for AI (e.g., AAAI, IJCAI), data mining (e.g., KDD, WWW, WSDM), and NLP (e.g., ACL). She is the web chair of WSDM'22 and senior program committee member of AAAI'22.