What If Moderation Didn't Mean Suppression? A Case for Personalized Content Transformation
Rayhan Rashed, Farnaz Jahanbakhsh
摘要
Centralized content moderation paradigm both falls short and overreaches: 1) it fails to account for the subjective nature of harm, and 2) it acts with blunt suppression in response to content deemed harmful, even when such content can be salvaged. We first investigate this through formative interviews, documenting how seemingly benign content becomes harmful due to individual life experiences. Based on these insights, we developed DIY-MOD, a browser extension that operationalizes a new paradigm: personalized content transformation. Operating on a user’s own definition of harm, DIY-MOD transforms sensitive elements within content in real-time instead of suppressing the content itself. The system selects the most appropriate transformation for a piece of content from a diverse palette—from obfuscation to artistic stylizing—to match the user’s specific needs while preserving the content’s informational value. Our two user studies demonstrate that this approach increases users’ sense of agency and safety, enabling them to engage with content and communities they previously needed to avoid.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper18
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Direct Preference Optimization: Your Language Model is Secretly a Reward ModelRafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning 等NeurIPS 2023 · 被引用 10,924 次
- MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsDeyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li 等ICLR 2024 · 被引用 3,079 次
- MAT: Mask-Aware Transformer for Large Hole Image InpaintingWenbo Li, Zhe Lin, Kun Zhou, Lu Qi 等CVPR 2022 · 被引用 382 次
- RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model FeedbackYufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian 等ICML 2024 · 被引用 135 次
相关 Paper
- DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity CensorshipYaqiong Li, Peng Zhang, Hansu Gu, Tun Lu 等CSCW 2025 · 被引用 6 次
- Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and LaborShagun Jhaver, Alice Qian Zhang, Quan Ze Chen, Nikhila Natarajan 等CSCW 2023 · 被引用 87 次
- Understanding User Needs and Attitudes for Privacy Protection Tools in Online Visual Content SharingChun-Wei Chiang, Harry Yizhou Tian, Ming YinCSCW 2025 · 被引用 4 次
- The Unsung Heroes of Facebook Groups Moderation: A Case Study of Moderation Practices and ToolsTina Kuo, Alicia Hernani, Jens GrossklagsCSCW 2023 · 被引用 25 次
- "Ignorance is not Bliss": Designing Personalized Moderation to Address Ableist Hate on Social MediaSharon Heung, Lucy Jiang, Shiri Azenkot, Aditya VashisthaCHI 2025 · 被引用 14 次
