Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model

TL;DR

This paper presents an unsupervised cyberbullying detection model that leverages multi-modal features and a Gaussian Mixture Model to identify bullying behavior without labeled data, addressing limitations of supervised methods.

Contribution

The work introduces a novel unsupervised detection approach combining representation learning and Gaussian Mixture Models, outperforming existing unsupervised methods and rivaling supervised models.

Findings

Outperforms state-of-the-art unsupervised models

Achieves competitive performance with supervised models

Utilizes multi-modal features including text, network, and time

Abstract

Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeling process is often time-consuming and labor-intensive; (2) Current labeling guidelines may not be generalized to future instances because of different language usage and evolving social networks. To address these limitations, this work introduces a principled approach for unsupervised cyberbullying detection. The proposed model consists of two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time. (2) A multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The proposed model jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.