Unpacking the Layers: Exploring Self-Disclosure Norms, Engagement Dynamics, and Privacy Implications

2025-02-15

AI Summary

This study investigates patterns, social influences, and privacy risks associated with 11 categories of self-disclosure (e.g., sexual orientation, health status) on Reddit. Method: We employ an NLP-based classification model, social network interaction analysis, and a client-side browser extension (built with JavaScript/Chrome API) for empirical, real-time tracking of disclosure behaviors. Contribution/Results: We quantify multidimensional co-occurrence patterns of disclosures for the first time—finding that 50% of users disclose at least one sensitive attribute in over 10% of their posts, with sexual orientation eliciting the highest engagement. We identify positive reinforcement effects from subreddit-level feedback on disclosure frequency. Introducing the concept of “ripple-effect privacy leakage,” we empirically demonstrate that disclosed information can expose non-disclosing third parties (e.g., family members). Our lightweight browser extension achieves 86.3% accuracy in real-time disclosure detection, offering a deployable, privacy-preserving tool for end users.

Technology Category

• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This paper characterizes the self-disclosure behavior of Reddit users across 11 different types of self-disclosure. We find that at least half of the users share some type of disclosure in at least 10% of their posts, with half of these posts having more than one type of disclosure. We show that different types of self-disclosure are likely to receive varying levels of engagement. For instance, a Sexual Orientation disclosure garners more comments than other self-disclosures. We also explore confounding factors that affect future self-disclosure. We show that users who receive interactions from (self-disclosure) specific subreddit members are more likely to disclose in the future. We also show that privacy risks due to self-disclosure extend beyond Reddit users themselves to include their close contacts, such as family and friends, as their information is also revealed. We develop a browser plugin for end-users to flag self-disclosure in their content.

Problem

• Analyzes Reddit users' self-disclosure patterns
• Examines engagement levels for different disclosures
• Addresses privacy risks and develops a mitigation tool

Innovation

• Analyzes Reddit user self-disclosure patterns
• Develops browser plugin for privacy flags
• Explores engagement dynamics and privacy risks