DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship

作者：Yaqiong Li, Peng Zhang, Hansu Gu, Tun Lu, Siyuan Qiao, Yu‐Bo Shu, Yiyang Shao, Ning Gu · 发表于：Proceedings of the ACM on Human-Computer Interaction · 年份：2025 · DOI：10.1145/3710959 · 被引用次数：6 · 研究领域：Hate Speech and Cyberbullying Detection、Adversarial Robustness in Machine Learning、Ethics and Social Impacts of AI

Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process, wherein detection is just an initial task and a user can have further needs such as rationale understanding and content modification. For this problem, we conduct a need-finding study to investigate people's diverse needs in toxicity censorship and then build a ChatGPT-based censorship tool named DeMod accordingly. DeMod is equipped with the features of explainable De tection and personalized Mod ification, providing fine-grained detection results, detailed explanations, and personalized modification suggestions. We also implemented the tool and recruited 35 Weibo users for evaluation. The results suggest DeMod's multiple strengths like the richness of functionality, the accuracy of censorship, and ease of use. Based on the findings, we further propose several insights into the design of content censorship systems.