DATE: Thu, Feb 2, 2017
TIME: 1 pm
TITLE: Cyberbullying Detection Using Social Network Anlaysis
PRESENTER: Qianjia Shy Huang
University of Ottawa

This talk will give a brief description of a previous research for cyberbullying detection and some ideas/trends for its future study. Cyberbullying is an important social challenge that takes place over a technical substrate. Thus it has attracted research interest across both computational and social science research communities. While the social science studies conducted via careful participant selection have shown the effect of personality, social relationships, and psychological factors on cyberbullying, they are often limited in scale due to manual survey or ethnographic study components. Computational approaches, on the other hand, have defined multiple automated approaches for detecting cyberbullying at scale, but mostly have only focused on the textual content of the messages exchanged. Unifying the two perspectives, the researchers investigated a holistic (social +text) approach for understanding and detecting cyberbullying. By analyzing the social relationship graph between users in an online social network and deriving features such as number of friends, network embeddedness, and relationship centrality, we found that: (1) multiple social characteristics are statistically different between the cyberbullying and non-bullying groups, thus supporting many, but not all, of the results found in previous survey-based bullying studies; and (2) analyzing such social network features surrounding the network can yield significant improvements in the accuracy of cyberbullying detection models as compared to purely text-based models. Finally, the newer types of cyberbullying from diversity social media and the current dataset from Instagram will be discussed.