DeMod让有毒内容检测更透明可定制，支持解释与个性化修改。
DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship
- 基于ChatGPT构建，实现可解释的毒性检测
- 35名微博用户测试显示准确率高且易用
- 适合需要理解理由并自定义修改内容的用户
尽管已有自动化工具支持社交帖子的毒性内容过滤，但多数仅聚焦于检测。毒性过滤是复杂流程，检测仅为初始步骤，用户还可能需要理解原因及修改内容。为此，我们开展需求调研，发现用户在毒性过滤中存在多样化需求，并据此构建了基于ChatGPT的过滤工具DeMod。该工具具备可解释检测与个性化修改功能，提供细粒度检测结果、详细解释及定制化修改建议。我们实现了该工具，并招募35名微博用户进行评估。结果表明，DeMod在功能丰富性、过滤准确性与易用性方面表现优异。基于研究发现，我们进一步提出内容过滤系统设计的若干洞察。
原文摘要 · Abstract (English)
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 needfinding 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 Detection and personalized Modification, 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.
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