Detecting harassment and defamation in cyberbullying with emotion-adaptive training

• URL: http://arxiv.org/abs/2501.16925v1
• Date: Tue, 28 Jan 2025 13:15:07 GMT
• Title: Detecting harassment and defamation in cyberbullying with emotion-adaptive training
• Authors: Peiling Yi, Arkaitz Zubiaga, Yunfei Long,
• Abstract summary: cyberbullying encompasses various forms, such as denigration and harassment, which celebrities frequently face. We first develop a celebrity cyberbullying dataset that encompasses two distinct types of incidents: harassment and defamation. We propose an emotion-adaptive training framework (EAT) that helps transfer knowledge from the domain of emotion detection to the domain of cyberbullying detection.
• Score: 10.769252194833625
• License:
• 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.

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