Seeking Help in the Digital Age: A Cross-Platform Analysis of Online Support Systems for Technology-Facilitated Abuse Victims

2026-07-23

AI Summary

This study addresses the challenges faced by victims of technology-facilitated abuse (TFA) when seeking online support, including information insecurity, lack of trauma-informed responses, and poor platform responsiveness. It introduces the first cross-platform, multidimensional evaluation framework, generating 11 structured queries grounded in real victim narratives to systematically assess response quality across search engines, peer forums, and conversational AI systems. Using qualitative coding, supervised classifiers, and automated metrics—spanning relevance, actionability, empathy, toxicity, and social engineering risk—the analysis reveals that over 65% of search results contain potentially malicious links, more than 20% of Reddit content exhibits toxicity, and general-purpose large language models outperform specialized chatbots. Nevertheless, no channel consistently delivers safe, trauma-informed support.

Abstract

Technology-facilitated abuse (TFA), the use of digital technologies to stalk, harass, monitor or threaten others, has become a pervasive form of interpersonal harm. As victims turn to online sources for guidance, responses can shape how they assess risks, interpret abuse, and choose protective actions. We present a large-scale evaluation of online support for TFA victims across three channels: web search, peer-support forums, and conversational AI systems. Drawing on a decade of victim narratives from r/Stalking, we use qualitative coding and supervised classifiers to construct a dataset of TFA queries spanning 11 categories of technology misuse. We simulate these queries across the three channels and evaluate responses using a unified framework spanning technical, social, and safety dimensions. The framework assesses relevance, accuracy, actionability, persuasiveness, and understandability, alongside platform risks and support characteristics, including social-engineering risk, toxicity, empathy, bias, risky guidance, and support information. We build and validate automated classifiers to scale the evaluation. Our findings reveal differences in support quality across platforms. Google Search and general-purpose LLMs provide more relevant and actionable guidance than Reddit discussions, yet none consistently provide safe, trauma-informed support. More than 65% of victim queries encounter potentially malicious links in search results, over 20% of Reddit discussions contain toxic responses, and conversational AI systems frequently fail to provide risk-aware guidance or concrete support resources. Surprisingly, domain-specific survivor-support chatbots underperform general-purpose LLMs across most dimensions. These findings expose weaknesses in digital support for TFA victims and highlight the need for safety-centered design, evaluation, and deployment of future support technologies.

Problem

• Technology-Facilitated Abuse
• Online Support Systems
• Digital Safety
• Victim Assistance
• AI Safety

Innovation

• technology-facilitated abuse
• online support systems
• conversational AI evaluation
• safety-centered design
• automated content classification