Reducing Privacy Risks in Online Self-Disclosures with Language Models

arXiv:2311.09538v2 Announce Type: replace
Abstract: 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 detection 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 detection, achieving over 65% partial span F$_1$. We further conduct an HCI user study, with …

abstract abstraction arxiv cs.cl cs.hc detection language language models media paper privacy protect risks social social media taxonomy through type