Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Recent years have witnessed a surge in research aimed at developing principled learning models to detect cyberbullying behaviors. These efforts have primarily focused on building a single generic classification model to differentiate bullying content from normal (non-bullying) content among all users. These models treat users equally and overlook idiosyncratic information about users that might facilitate the accurate detection of cyberbullying. In this paper, we propose a personalized cyberbullying detection framework, PI-Bully, that draws on empirical findings from psychology highlighting unique characteristics of victims and bullies and peer influence from like-minded users as predictors of cyberbullying behaviors. Our framework is novel in its ability to model peer influence in a collaborative environment and tailor cyberbullying prediction for each individual user. Extensive experimental evaluations on real-world datasets corroborate the effectiveness of the proposed framework.
Personalized Learning for Cyberbullying Detection
Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Traditional efforts are primarily devoted to building a single generic classification model for all users to differentiate bullying behaviors from the normal content [6, 3, 1, 2, 4]. Despite its empirical success, these models treat users equally and inevitably ignore the idiosyncrasies of users. Recent studies from psychology and sociology suggest that the occurrence of cyberbullying has a strong connection with the personality of victims and bullies embedded in the user-generated content, and the peer influence from like-minded users. In this paper, we propose a personalized cyberbullying detection framework PI-Bully with peer influence in a collaborative environment to tailor the prediction for each individual. In particular, the personalized classifier of each individual consists of three components: a global model that captures the commonality shared by all users, a personalized model that expresses the idiosyncratic personality of each specific user, and a third component that encodes the peer influence received from like-minded users. Most of the existing methods adopt a two-stage approach: they first apply feature engineering to capture the cyberbullying patterns and then employ machine learning classifiers to detect cyberbullying behaviors.
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- Award ID(s):
- 1719722
- Publication Date:
- NSF-PAR ID:
- 10067396
- Journal Name:
- Doctoral Consortium of the International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation
- Sponsoring Org:
- National Science Foundation
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