# Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI

arXiv: https://arxiv.org/abs/2412.15047 · HTML: https://mlsanity.com/paper/2412.15047/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 101 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement](https://mlsanity.com/paper/2204.13074/) (2022)
- [Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks](https://mlsanity.com/paper/2310.07879/) (2023)
- [A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems](https://mlsanity.com/paper/2010.12251/) (2020)
- [Multilingual Detection of Personal Employment Status on Twitter](https://mlsanity.com/paper/2203.09178/) (2022)
- [Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media](https://mlsanity.com/paper/1902.10680/) (2019)
- [An Empirical Assessment of Security and Privacy Risks of Web based-Chatbots](https://mlsanity.com/paper/2205.08252/) (2022)
- [Privacy in Crisis: A study of self-disclosure during the Coronavirus pandemic](https://mlsanity.com/paper/2004.09717/) (2020)
- [Do They Accept or Resist Cybersecurity Measures? Development and Validation of the 13-Item Security Attitude Inventory (SA-13)](https://mlsanity.com/paper/2204.03114/) (2022)

## Built on by (most cited first, 4 of 4)

- [Position: Privacy Is Not Just Memorization!](https://mlsanity.com/paper/2510.01645/) (2025)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)

## Abstract (arXiv)

In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to tell that this is me?). Prior work has sought to develop natural language processing (NLP) tools that help users identify potentially risky self-disclosures in their text, but none have been designed for or evaluated with the users they hope to protect. Absent this assessment, these tools will be limited by the social-technical gap: users need assistive tools that help them make informed decisions, not paternalistic tools that tell them to avoid self-disclosure altogether. To bridge this gap, we conducted a study with N = 21 Reddit users; we had them use a state-of-the-art NLP disclosure detection model on two of their authored posts and asked them questions to understand if and how the model helped, where it fell short, and how it could be improved to help them make more informed decisions. Despite its imperfections, users responded positively to the model and highlighted its use as a tool that can help them catch mistakes, inform them of risks they were unaware of, and encourage self-reflection. However, our work also shows how, to be useful and usable, AI for supporting privacy decision-making must account for posting context, disclosure norms, and users' lived threat models, and provide explanations that help contextualize detected risks.