Understanding Help Seeking for Digital Privacy, Safety, and Security

2026-01-16

AI Summary

This study addresses the critical gap in systematic understanding of user support ecosystems for digital privacy and security, where individuals often lack effective assistance when facing threats. By analyzing over one billion Reddit posts from the past four years, the research presents the first large-scale integration of qualitative content coding with fine-tuned large language models (LLMs) to automatically identify and label three million real-world help-seeking posts with high accuracy (93%). The analysis spans key topics including security tools, privacy settings, scams, and account compromises, offering a precise characterization of user求助 scenarios and community support patterns. These findings provide both a robust empirical foundation and methodological innovation for designing more effective user support systems in digital safety contexts.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Privacy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The complexity of navigating digital privacy, safety, and security threats often falls directly on users. This leads to users seeking help from family and peers, platforms and advice guides, dedicated communities, and even large language models (LLMs). As a precursor to improving resources across this ecosystem, our community needs to understand what help seeking looks like in the wild. To that end, we blend qualitative coding with LLM fine-tuning to sift through over one billion Reddit posts from the last four years to identify where and for what users seek digital privacy, safety, or security help. We isolate three million relevant posts with 93% precision and recall and automatically annotate each with the topics discussed (e.g., security tools, privacy configurations, scams, account compromise, content moderation, and more). We use this dataset to understand the scope and scale of help seeking, the communities that provide help, and the types of help sought. Our work informs the development of better resources for users (e.g., user guides or LLM help-giving agents) while underscoring the inherent challenges of supporting users through complex combinations of threats, platforms, mitigations, context, and emotions.

Problem

• digital privacy
• online safety
• cybersecurity
• help seeking
• user support

Innovation

• LLM fine-tuning
• help-seeking behavior
• digital privacy
• automated annotation
• large-scale social media analysis