Detecting harassment and defamation in cyberbullying with emotion-adaptive training

TL;DR

This paper introduces a new emotion-adaptive training framework that enhances the detection of various cyberbullying forms, including harassment and defamation, especially in low-resource settings, by transferring knowledge from emotion detection.

Contribution

It develops a diverse cyberbullying dataset and proposes EAT, a novel training method that significantly improves multi-class cyberbullying detection performance across multiple transformer models.

Findings

EAT improves macro F1, precision, and recall by 20% on average.

Transformer models perform well on explicit harassment detection but struggle with multi-class classification.

EAT enhances detection of indirect cyberbullying in low-resource scenarios.

Abstract

Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However, cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. Furthermore, suitable training data for these diverse forms of cyberbullying remains scarce. In this study, we first develop a celebrity cyberbullying dataset that encompasses two distinct types of incidents: harassment and defamation. We investigate various types of transformer-based models, namely masked (RoBERTa, Bert and DistilBert), replacing(Electra), autoregressive (XLnet), masked&permuted (Mpnet), text-text (T5) and large language models (Llama2 and Llama3) under low source settings. We find that they perform competitively on explicit harassment binary detection. However, their performance is substantially lower on harassment and denigration multi-classification tasks. Therefore, we propose an emotion-adaptive training framework (EAT) that helps transfer knowledge from the domain of emotion detection to the domain of cyberbullying detection to help detect indirect cyberbullying events. EAT consistently improves the average macro F1, precision and recall by 20% in cyberbullying detection tasks across nine transformer-based models under low-resource settings. Our claims are supported by intuitive theoretical insights and extensive experiments.