  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 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, which is rephrasing disclosures into less specific
terms while preserving their utility, e.g., "Im 16F" to "I'm a teenage girl".
We explore various fine-tuning strategies, and our best model can generate
diverse abstractions that moderately reduce privacy risks while maintaining
high utility according to human evaluation. To help users in deciding which
disclosures to abstract, we present a task of rating their importance for
context understanding. Our fine-tuned model achieves 80% accuracy, on-par with
GPT-3.5. Given safety and privacy considerations, we will only release our
corpus and models to researcher who agree to the ethical guidelines outlined in
Ethics Statement.
