Paper Info
Title
A Weakly Supervised Approach for Adaptive Detection of Cyberbullying Roles.
Abstract
As human communication increasingly occurs through online social networks, online harassment and cyberbullying are becoming a serious social health threat. In this talk, I will introduce an automated, data-driven method for adaptive detection of bullying roles. The model represents whether social media users are instigating bullying, whether they are victimized by bullying, and how indicative certain key phrases are of bullying behavior. The algorithm simultaneously estimates these variables by extrapolating from social media graph data and an expert-provided set of highly indicative bullying phrases. This weak supervision provides training input without requiring excessive effort from human annotators. I will describe quantitative and qualitative experiments on three social media datasets from networks with frequent incidences of cyberbullying: Twitter, Ask.fm, and Instagram. I will conclude by describing a vision of a future where technology helps prevent and mitigate the harm of cyberviolence, placing detection algorithms like the main topic of this talk in context.
Year
DOI
Venue
2016
10.1145/3002137.3002142
CyberSafety@CIKM
Keywords
Field
DocType
Machine Learning, Online social networks
Graph,World Wide Web,Internet privacy,Social media,Online harassment,Social network,Information retrieval,Computer science,Harm,Human communication,Social determinants of health
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Bert Huang156339.09