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

Digital Support Systems for Technology-Facilitated Abuse Victims Often Provide Unsafe or Inaccurate Guidance

When individuals experience technology-facilitated abuse (TFA)—the use of digital tools to stalk, harass, or monitor others—they often turn to the internet for immediate guidance. Researchers have studied how victims fare on Google, Reddit, and AI chatbots. They sought to identify safe paths to recovery.

While AI and search engines tend to provide better technical advice than peer forums, the study finds that many systems provide dangerous suggestions. These suggestions could destroy vital evidence or increase physical danger. Furthermore, a significant portion of search results lead victims toward malicious websites.

The Hidden Risks of Digital Help-Seeking

The core challenge lies in the complexity of modern abuse. TFA involves the weaponization of everyday tools like smartphones, smart-home devices, and financial applications. Formal support providers often lack specialized digital-safety expertise. Consequently, victims frequently bypass traditional channels. They turn to online sources to interpret their experiences and identify protective actions.

This creates a massive, unvetted information ecosystem. Navigating this space is like walking through a minefield. The signs pointing toward safety might actually be decoys leading to further harm. If a digital system provides incorrect or unsafe advice, it does more than fail to help. It can actively shape a victim's perception of risk. This influences life-altering decisions regarding their personal safety.

Understanding the Digital Ecosystem

To analyze this landscape, the authors first had to define exactly what constitutes digital abuse. They developed a taxonomy of 11 technology-misuse categories. These range from "Surveillance and Tracking Technologies" (such as GPS trackers or AirTags) to "Image and Video Manipulation" (including deepfakes).

The researchers built their study upon a decade of authentic narratives from the r/Stalking subreddit. They used qualitative coding and supervised classifiers (automated algorithms that categorize data based on learned patterns) to transform these raw stories. This yielded a dataset of 2,797 victim-authored queries. These queries serve as the "test cases" for the study.

The authors then simulated help-seeking across three distinct environments: 1. Web Search: Retrieving the top 10 Google Search results for each query. 2. Peer-Support Forums: Analyzing existing comment threads on Reddit. 3. Conversational AI: Testing general-purpose Large Language Models (LLMs) like GPT-5 and Claude, as well as domain-specific chatbots like HopeChat and Ruth AI.

To ensure the evaluation was rigorous, the authors implemented a "Unified Evaluation Framework." This framework splits the assessment into two halves .

The Technical Dimension measures relevance, accuracy, actionability (the ability to provide clear, executable steps), persuasiveness, and understandability. The Social Dimension measures empathy, autonomy (the degree to which a user's choice is respected), bias, and safety risks like toxicity (hostile or harmful language) or malicious links.

Measuring Technical Utility and Social Safety

The study's results reveal a stark divide between how well a system answers a question and how safe that answer actually is.

On the technical side, Google Search and general-purpose LLMs outperformed Reddit. The authors report that 91% of Google queries received at least one relevant webpage within the top 10 results [Figure 3a]. This means most searchers find topically related content quickly. In contrast, only 52% of Reddit queries received relevant or helpful comments [Figure 3a]. When it comes to actionability, LLMs were the leaders. Some generated actionable guidance for 45–75% of queries [Figure 6d].

However, technical accuracy is only half the story. The paper finds that many systems provide "Damaging Guidance." This refers to advice that is technically plausible but ignores unique safety needs. For example, Google Search and LLMs frequently recommended deleting accounts or resetting devices. They did this without warning that such actions could destroy evidence. Such evidence is often necessary for legal prosecution or police investigations.

The social risks are equally concerning. The authors report that 65.5% of victim queries via Google Search encountered at least one malicious secondary URL . These were often phishing links (fraudulent sites designed to steal information). On Reddit, over 20% of queries were met with toxic responses [Figure 8b]. These included insults or threats. Even the specialized chatbots struggled. Domain-specific bots like HopeChat and Ruth often scored very low on empathy and humanization compared to general-purpose models [Figure 9a].

The Gap Between Intelligence and Empathy

These findings change how we view the "intelligence" of AI in sensitive domains. High technical performance does not automatically translate to safe, trauma-informed support.

The data suggests a widening gap. General-purpose LLMs are becoming better at providing the "how-to" of digital security. However, they are failing to provide the "why" and the "stay safe" context. A model might correctly explain how to block a user. But if it recommends doing so in a way that triggers an abuser to escalate violence, the model has failed.

Furthermore, the study highlights a surprising trend. Domain-specific survivor-support chatbots consistently underperformed general-purpose LLMs across most evaluation dimensions. This suggests that simply training a model on "supportive" language is not enough. It does not automatically overcome the challenges of providing accurate, risk-aware technical guidance.

Limits of the Framework

The authors note several constraints to their findings. The evaluation of technical accuracy was limited to 50 queries. This is because constructing "gold-standard" reference answers required intense manual effort from experts. Consequently, high-level accuracy metrics for the entire dataset rely heavily on automated, LLM-based judges. While the authors validated these against humans, they are not a perfect substitute.

Additionally, the study relies on public Reddit data. While these narratives are authentic, they may not capture the full spectrum of TFA experiences. Many abuses occur in private or highly encrypted spaces. Finally, the framework evaluates responses to queries. It does not explore the long-term psychological impact of interacting with these systems over multiple sessions.

Figures from the paper