Bullying, once limited to physical spaces (e.g., schools, workplaces or sports fields) and particular times of the day (e.g., school hours), can now occur anytime, anywhere. Cyberbullying can take many forms; however, it typically refers to repeated and hostile behavior performed in an effort to intentionally and repeatedly harass or harm individuals. The consequences of cyberbullying can be devastating.
This tutorial presents a systematic review of approaches to detect, characterize, and mitigate cyberbullying behavior on online social media. First, we discuss the concept of cyberbullying, and the related nomenclature, by drawing from the social and psychological sciences. Then, we perform an in–depth review of the state–of–the–art cyberbullying research. We characterize the phenomenon of bullying on social media, and present recent computational approaches for the detection, quantification, and mitigation of cyberbullying. We subsequently discuss the role of content and network structure of online social media interactions to these ends.
Throughout the tutorial, we highlight the open challenges that need to be addressed in understanding, predicting, and preventing cyberbullying behavior, as well as promising research directions for researchers interested in this area.
The tutorial will be held at the 12th International AAAI Conference on Web and Social Media in Stanford, California, USA on June 25th, 2018.
The tutorial slides can be found here.
The tutorial includes an interactive session focused on exploring different types of bullying online and the effects of human annotation on cyberbullying detection. Participants will jointly evaluate (and engage in discussion) with the tutors sample content from Twitter and Instagram. Please find the worksheets for this session here.
This tutorial targets academic, industry and government researchers and practitioners with interest in user behavior modeling, social media and social network analysis, graph mining, and detection and prediction algorithms. Beginners will learn the basics of state–of–the–art methods in this area, whereas experts in the area will have a chance for an in–depth review of both platform–specific techniques and platform–independent algorithms to detect online bullying behavior. As a result, this tutorial should appeal to researchers of several disciplines.
Charalampos Chelmis is an Assistant Professor of Computer Science at the University at Albany, State University of New York. His research focus is on Network Science and Big Data analytics. Before joining the University at Albany, he was Senior Research Associate at the University of Southern California. He obtained his Ph.D. from the University of Southern California in 2013.
Daphney-Stavroula Zois is an Assistant Professor in Electrical and Computer Engineering at the University at Albany, State University of New York. She specializes in Machine Learning and Signal Processing methods for decision-making under uncertainty. Before joining UAlbany, she was a Postdoctoral Researcher at the University of Illinois at Urbana Champaign. She obtained her Ph.D. from the University of Southern California in 2014.