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Aparna Sankaran

October 17 @ 9:30 am - 10:30 am MDT

Thesis Information

Title: BullyNet: Unmasking Cyberbullies on Social Networks

Program: Master of Science in Computer Science

Advisor: Dr. Gaby Dagher, Computer Science

Committee Members: Dr. Bogdan Dit, Computer Science and Dr. Min Long, Computer Science

Abstract

Social media has changed the way people communicate with each other, and consecutively affected people’s ability to empathize in both positive and negative ways. One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying given that online records typically live on the internet for quite a long time and are hard to control. In this thesis, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network. BullyNet analyzes each tweet to determine its relation to cyberbullying, while considering the context in which the tweet exists in order to optimize its bullying score. We also propose a centrality measure to detect cyberbullies from a cybebullying signed network, and we show that it outperforms other existing measures. We evaluate our method on a dataset of 5.6 million tweets we synthesized and labeled. Our experimental results show that the proposed BullyNet algorithm can detect cyberbullies with high accuracy, while being scalable with respect to the number of tweets.