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arXiv · 2402.16458

ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection

Peiling Yi·Arkaitz Zubiaga
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Abstract

Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to bias-inducing words without impacting overall model performance. We explore ID-XCB on two popular session-based cyberbullying datasets along with comprehensive ablation and generalisation studies. We show that ID-XCB learns robust cyberbullying detection capabilities while mitigating biases, outperforming state-of-the-art debiasing methods in both performance and bias mitigation. Our quantitative and qualitative analyses demonstrate its generalisability to unseen data.

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Peiling Yi, Arkaitz Zubiaga. 2024-02-26. ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection. https://arxiv.org/abs/2402.16458

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