Abstract

Cyberbullying is a major issue on online social platforms, and can have prolonged negative psychological impact on both the bullies and their targets. Users can be characterized by their involvement in cyberbullying according to different social roles including victim, bully, and victim supporter. In this work, we propose a social role detection framework to understand cyberbullying on online social platforms, and select a dataset that contains users’ records on both Instagram and Ask.fm as a case study. We refine the traditional victim-bully framework by constructing a victim-bully-supporter network on Instagram. These social roles are automatically identified via ego comment networks and linguistic cues of comments. Additionally, we analyze the consistency of users’ social role within Instagram and compare users’ behaviors on Ask.fm. Our analysis reveals the inconsistency of social roles both within and across …

Metadata

publication

Companion Proceedings of The 2019 World Wide Web Conference, 183-188, 2019

year

2019

publication date

2019/5/13

authors

Hsien-Te Kao, Shen Yan, Di Huang, Nathan Bartley, Homa Hosseinmardi, Emilio Ferrara

link

https://dl.acm.org/doi/abs/10.1145/3308560.3316505

conference

Companion Proceedings of The 2019 World Wide Web Conference

pages

183-188

publisher

ACM