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

Source: arXiv:2607.21549 · Published 2026-07-23 · By Nowshin Tabassum, Solomon G. Dandekar, Morgan PettyJohn, Tim Ryan, Minjaal Raval, Rachel Voth Schrag et al.

TL;DR

This paper addresses the problem of evaluating online support systems for victims of technology-facilitated abuse (TFA), a pervasive form of digital interpersonal harm involving stalking, surveillance, harassment, and control using digital technologies. Victims increasingly seek help online via web search, peer-support forums, and conversational AI, but the quality, safety, and trauma-informative nature of responses remain poorly understood. The authors construct a large, authentic dataset of 2,797 victim-authored TFA help-seeking queries sourced from a decade of r/Stalking Reddit narratives. They develop a taxonomy of 11 categories of technology misuse and simulate victim queries across three major support ecosystems: Google Search, Reddit peer-support comments, and conversational AI systems (including general-purpose LLMs and domain-specific survivor chatbots).

They design a novel unified evaluation framework combining technical (relevance, accuracy, actionability, persuasiveness, understandability) and social/safety dimensions (toxicity, empathy, bias, risk-informed guidance, support resources, and social engineering risk). Using a hybrid methodology combining expert annotation and LLM-assisted scoring, they conduct a large-scale cross-platform empirical assessment. Key findings reveal that Google Search results and general-purpose LLMs generally provide more relevant and actionable guidance than Reddit peers and domain-specific chatbots, yet all platforms fail to consistently deliver safe, trauma-informed support. Notably, over 65% of Google search results exposed victims to potentially malicious links, Reddit comments had 20% toxicity rates, and AI systems frequently missed risk-aware guidance or concrete support resources. Domain-specific TFA survivor chatbots surprisingly underperformed relative to general LLMs across most measured dimensions. The findings highlight critical gaps and risks in current digital support ecosystems and motivate safety-centered design and rigorous, victim-centered evaluation of future TFA support technologies.

Key findings

• From 3,267 Reddit posts, 5,926 help-seeking questions were extracted; after classification, 2,797 victim-authored TFA queries were identified representing real victim experiences.
• Technology misuse categories span 11 classes; Social Media & Messaging Platforms comprised 40.3% of victim queries, Image & Video Manipulation only 1.2%.
• Google Search results achieved 87% automated relevance classification accuracy on 27,162 cleaned webpages retrieved for victim queries.
• More than 65% of victim queries returned search results containing potentially malicious links posing social engineering risks.
• Over 20% of Reddit peer-support comments were identified as toxic using Perspective API metrics, affecting victims’ help-seeking safety.
• General-purpose LLMs (GPT-5, Claude Opus 4.1, Gemini-2.5-Pro) provided more relevant and actionable responses than Reddit and domain-specific chatbots (HopeChat, Ruth AI).
• Domain-specific survivor chatbots consistently performed worse than general LLMs on empathy, risk-aware guidance, support information, and safety metrics.
• No platform reliably provided safe, trauma-informed support; many responses lacked risk-informed guidance, supportive resources, or avoided victim-blaming language.

Threat model

N/A — This work primarily evaluates the quality and safety of online support responses for TFA victims rather than modeling specific attacker capabilities. The implicit adversary is the ecosystem of potential misinformation, toxic community responses, and platforms that unintentionally expose victims to malicious content or unsafe guidance.

Methodology — deep read

1. Threat Model & Assumptions: The adversary is not explicitly modeled since this is an evaluation of victim help-seeking support systems rather than attacker capabilities. The focus is on whether support platforms provide safe, accurate, actionable, and trauma-informed guidance without exposing victims to additional risks like toxic language or malicious links.

2. Data: Data was collected from the r/Stalking subreddit, a decade-long archive (2014-2024) of public posts and comments discussing TFA experiences. The dataset includes 3,266 posts and 32,162 comments. Posts with victim narratives were manually annotated for technology misuse categories, TFA relevance, and poster roles. Using qualitative coding and interdisciplinary annotation teams, an 11-category taxonomy of technology misuse was established. Prompt-based few-shot classifiers (using GPT-OSS 20B) were developed to label posts across categories achieving >0.9 accuracy and F1 >0.85. Victim help-seeking questions were extracted using LLMs (Llama3.3 chosen for coverage over GPT-4) yielding 5,926 questions, filtered to 2,797 TFA-relevant victim-authored queries after classification with Llama3.3 (F1=0.86 for TFA relevance, 0.96 for poster role).

3. Architecture / Algorithm: Multi-label classifiers for 11 technology misuse categories were implemented via prompt-based few-shot classification on GPT-OSS 20B. Question extraction employed abstractive prompts with Llama3.3 to parse victim narratives. For evaluation, a hybrid human-LLM approach using chain-of-thought prompting with Llama3.3 and ensemble LLM-as-a-judge models assessed technical and social metrics across thousands of items.

4. Training Regime: Not traditional ML training but prompt engineering with iterative refinement and human validation was used. Validation sets of manually labeled samples were used to tune and confirm classifier accuracy. Detailed inter-coder agreement statistics supported robustness.

5. Evaluation Protocol: Three platforms were evaluated:

   • Google Search: top 10 URLs per query (27,970 webpages) scraped and cleaned. A relevance classifier was validated against human labels (87% accuracy) before large-scale inference. Linked URLs on pages were also analyzed for social engineering risks.
   • Reddit forums: 17,714 comments tied to victim queries were assessed for technical relevance, toxicity (via Perspective API), and social support cues.
   • Conversational AI: three general LLMs (GPT-5, Claude Opus 4.1, Gemini-2.5-Pro) and two domain-specific chatbots (HopeChat, Ruth AI) were tested over a representative 50-query subset sampled proportionally across tech misuse categories; interactions were freshened to avoid carryover.

Six technical metrics (relevance, accuracy, actionability, persuasiveness, understandability) plus social/safety metrics (toxicity, empathy, voice & choice, bias, risk-aware guidance, support info, social engineering risk) were applied. Human annotations and social worker expertise informed social dimensions. Automated LLM evaluators were calibrated on gold sets before full deployment. Non-relevant responses were excluded from some analyses to avoid skew.

6. Reproducibility: The authors released codebooks, evaluation prompts, and code at https://github.com/UTA-SPLAB/tech-abuse-clinic to facilitate replication. Reddit data is public but collected with scraping tools and may be subject to deletions over time. LLM weights are closed-source or commercial models.

Concrete Example: For a victim query about monitoring of Snapchat messages, Google Search browses top 10 URLs, scrapes text, and uses human-validated classifiers to identify relevance and actionable advice. Reddit comments responding to the original post are scored for toxicity and relevance. Concurrently, LLMs and chatbots are prompted on the same query and their textual responses rated for accuracy, empathy, and risk-aware guidance by humans and LLM judges. Results are synthesized to compare quality across platforms for this type of TFA abuse.

Technical innovations

• Development of a victim-centered taxonomy of 11 distinct technology misuse categories grounding TFA help-seeking needs via an interdisciplinary mixed-methods pipeline combining qualitative coding and prompt-based multi-label classification.
• A unified, scalable evaluation framework integrating technical (relevance, accuracy, actionability, persuasiveness, understandability) and social/safety (toxicity, empathy, bias, risk-informed guidance, support information, social engineering risk) dimensions tailored for cross-platform comparisons of online TFA support systems.
• Hybrid human and LLM-assisted annotation methodologies employing chain-of-thought prompting and multi-judge ensembles to reliably rate thousands of victim query responses across heterogeneous sources (Google Search webpages, Reddit comments, LLM/chatbot conversational replies).
• Comprehensive large-scale simulation of authentic victim TFA queries across three online support ecosystems including Google Search, Reddit peer-support, and conversational AI (general LLMs and survivor-specific chatbots), enabling direct, systematic cross-platform quality and safety benchmarking.

Datasets

• r/Stalking subreddit — 3,266 posts, 32,162 comments spanning 2014–2024 — public Reddit data collected via API and Pushshift archives
• Victim-authored TFA query dataset — 2,797 queries extracted from r/Stalking posts using LLM-based question extraction and classification (filtered for victim and TFA relevance)

Baselines vs proposed

• Google Search webpages: relevance classification accuracy = 87% on human-annotated validation set of 90 webpage-query pairs.
• Reddit peer-support comments: toxicity rate > 20% measured by Perspective API compared to lower toxicity in other platforms.
• General-purpose LLMs (GPT-5, Claude Opus 4.1): higher relevance, actionability, and risk-aware guidance scores compared with domain-specific survivor chatbots (HopeChat, Ruth AI) which underperformed on empathy and support information metrics.
• Domain-specific chatbots underperformed general LLMs across nearly all evaluation dimensions (technical and social).

Figures from the paper

Figures are reproduced from the source paper for academic discussion. Original copyright: the paper authors. See arXiv:2607.21549.

Fig 1: Overview of our Methodology Pipeline

Fig 2: Distribution and nature of help-seeking questions

Fig 3 (page 3).

Fig 4 (page 3).

Fig 5 (page 3).

Fig 6 (page 3).

Limitations

• The evaluation does not include adversarial testing of support systems against malicious attackers attempting to manipulate victim queries or responses.
• Question extraction relies on LLM-based abstractive methods, which may miss implicit or nuanced victim questions, potentially biasing coverage.
• Social and trauma-informed metrics, while expert-informed, remain partially subjective and may vary across coders or cultures.
• Conversational AI evaluation was limited to a sampled subset (50 queries) due to resource constraints, reducing statistical power for some analyses.
• Some Reddit data may be incomplete due to post deletions and archival gaps, particularly in early years (2014-2015).
• Google Search evaluation excludes paywalled or inaccessible URLs, which may bias the sample of retrieved support content.

Open questions / follow-ons

• How can future digital support systems for TFA victims be designed to integrate trauma-informed care principles and risk-aware guidance natively?
• What automated safeguards could be implemented in web search and peer-support forums to detect and mitigate toxic or malicious content exposure to vulnerable victims?
• How might personalized victim context or interaction histories improve conversational AI’s ability to provide tailored, safe, and actionable TFA support?
• Can ongoing adversarial evaluation frameworks be developed to rigorously test online support ecosystems for resilience against manipulation by abusers or malicious actors?

Why it matters for bot defense

Bot-defense and CAPTCHA practitioners can draw several insights from this analysis of online support for TFA victims. First, the high prevalence of potentially malicious links (over 65%) in Google Search results highlights a significant attack surface where malicious actors can target vulnerable populations with phishing, scams, and malware. CAPTCHA systems or bot-detection mechanisms integrated into search results or support forums might help reduce automated propagation of harmful links or content.

Second, the notable toxicity levels (>20%) in peer-support forums emphasize the risk of hostile or manipulative user-generated content undermining victim safety and trust. Moderation tools leveraging nuanced toxicity detection, combined with mechanisms like challenges or rate limits to filter bot-driven or manipulative posts, could improve the safety of these spaces.

Finally, the underperformance of domain-specific chatbots in delivering trauma-informed, safe guidance underscores technical gaps in current conversational AI safety and alignment, an area where bot-defense experts could intersect by developing frameworks to monitor model outputs for safety, bias, and misinformation when deployed in sensitive contexts. Thus, this work serves as a cautionary case study revealing the complexity and risk in digital help-seeking ecosystems that CAPTCHAs and bot-defense tools might mitigate by elevating trust and safety.

Cite

@article{arxiv2607_21549,
  title={ Seeking Help in the Digital Age: A Cross-Platform Analysis of Online Support Systems for Technology-Facilitated Abuse Victims },
  author={ Nowshin Tabassum and Solomon G. Dandekar and Morgan PettyJohn and Tim Ryan and Minjaal Raval and Rachel Voth Schrag and Mohit Singhal and Shirin Nilizadeh },
  journal={arXiv preprint arXiv:2607.21549},
  year={ 2026 },
  url={https://arxiv.org/abs/2607.21549}
}

Read the full paper

• arXiv:2607.21549 (abstract)
• arXiv:2607.21549 (PDF)