Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy associated with online self-disclosure through identification and abstraction. We develop a taxonomy of 19 self-disclosure categories, and curate a large corpus consisting of 4.8K annotated disclosure spans. We then fine-tune a language model for identification, achieving over 75% in Token F$_1$. We further conduct a HCI user study, with 82\% of participants viewing the model positively, highlighting its real world applicability. Motivated by the user feedback, we introduce the task of self-disclosure abstraction. We experiment with both one-span abstraction and three-span abstraction settings, and explore multiple fine-tuning strategies. Our best model can generate diverse abstractions that moderately reduce privacy risks while maintaining high utility according to human evaluation.

翻译：自我披露在社交媒体互动中虽普遍且有益，但也带来隐私风险。本文率先通过识别与抽象化来保护在线自我披露中的用户隐私。我们构建了包含19类自我披露的分类体系，并整理了一个包含4800个标注披露片段的大规模语料库。随后微调语言模型进行识别，在Token F₁指标上达到75%以上。我们进一步开展了人机交互用户研究，82%的参与者对模型持积极态度，凸显其现实应用价值。受用户反馈启发，我们提出自我披露抽象化任务。实验涵盖单片段抽象与三片段抽象两种设置，并探索多种微调策略。根据人工评估，最优模型能生成多样化的抽象结果，在保持高实用性的同时适度降低隐私风险。