Title: Cyber Bullying Detection Using Social and Textual Analysis
Abstract: Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.
Publication Year: 2014
Publication Date: 2014-11-03
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 197
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot