ToxVis: Enabling Interpretability of Implicit vs. Explicit Toxicity Detection Models with Interactive Visualization
This work addresses the problem of online hate speech moderation for content moderators and platforms, offering an explainable tool, but it is incremental as it combines existing models and techniques.
The researchers tackled the challenge of detecting hate speech, including implicit forms, by developing ToxVis, a tool that classifies text into implicit, explicit, or non-hateful categories using fine-tuned transformer models like RoBERTa, XLNET, and GPT-3, and provides visual explanations for the classifications to aid content moderation.
The rise of hate speech on online platforms has led to an urgent need for effective content moderation. However, the subjective and multi-faceted nature of hateful online content, including implicit hate speech, poses significant challenges to human moderators and content moderation systems. To address this issue, we developed ToxVis, a visually interactive and explainable tool for classifying hate speech into three categories: implicit, explicit, and non-hateful. We fine-tuned two transformer-based models using RoBERTa, XLNET, and GPT-3 and used deep learning interpretation techniques to provide explanations for the classification results. ToxVis enables users to input potentially hateful text and receive a classification result along with a visual explanation of which words contributed most to the decision. By making the classification process explainable, ToxVis provides a valuable tool for understanding the nuances of hateful content and supporting more effective content moderation. Our research contributes to the growing body of work aimed at mitigating the harms caused by online hate speech and demonstrates the potential for combining state-of-the-art natural language processing models with interpretable deep learning techniques to address this critical issue. Finally, ToxVis can serve as a resource for content moderators, social media platforms, and researchers working to combat the spread of hate speech online.