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REVIEW 1 major objections 8 minor 85 references

Victims of tech-abuse seeking help online get relevant but unsafe guidance — phishing links, toxic forum replies, and advice that can destroy evidence — and no channel is consistently safe.

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2026-08-01 07:05 UTC pith:BI2X6LNW

load-bearing objection A valuable victim-centered dataset and framework, but the Reddit arm of the cross-platform comparison is structurally confounded and needs a fix before the headline claims can be trusted. the 1 major comments →

arxiv 2607.21549 v1 pith:BI2X6LNW submitted 2026-07-23 cs.CY cs.SI

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

Nowshin Tabassum , Solomon G. Dandekar , Morgan PettyJohn , Tim Ryan , Minjaal Raval , Rachel Voth Schrag , Mohit Singhal , Shirin Nilizadeh This is my paper
classification cs.CY cs.SI
keywords technology-facilitated abuseonline help-seekingweb search safetypeer-support forumsconversational AItrauma-informed supportphishing exposurevictim-centered evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Technology-facilitated abuse — stalking, harassment, surveillance, and control carried out through phones, social media, trackers, financial apps, and smart devices — drives many victims to the internet for help when formal support feels inaccessible. This paper asks whether that help is actually safe. Using 2,797 real victim-authored questions drawn from a decade of r/Stalking narratives, it simulates help-seeking across Google Search, Reddit, and five conversational AI systems, grading responses on technical quality (relevance, accuracy, actionability, persuasiveness, understandability) and on safety (malicious-link exposure, toxicity, empathy, bias, risk-aware guidance, and support referrals). The central claim is that no channel consistently provides 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 even the best AI systems give 'damaging guidance' — technically plausible advice that can destroy evidence or escalate danger — in about one response in five. The stakes: for a population already hesitant to seek formal help, a search result or chatbot answer can shape a safety-critical decision, so the finding reframes the act of seeking help itself as a source of new risk.

Core claim

The paper's central claim is that online help for victims of technology-facilitated abuse can actively introduce new harm, not merely fail to help. Google Search and general-purpose LLMs beat peer forums on technical quality, yet no channel clears the safety bar. Over 65% of victim queries encountered malicious links in Google results, over 20% of Reddit threads contained toxic comments, and even the strongest AI systems produced 'Damaging Guidance' — plausible advice that would destroy evidence or escalate risk — roughly once in five responses. Surprisingly, survivor-support chatbots underperformed general-purpose LLMs across nearly every dimension — a design problem, not a knowledge gap.

What carries the argument

The load-bearing object is the victim-centered query corpus: 2,797 real help-seeking questions extracted from a decade of r/Stalking narratives through LLM-assisted extraction and validated classifiers, spanning 11 technology-misuse categories. Around it the paper builds a Unified Evaluation Framework that grades every response twice — on five technical qualities (relevance, accuracy, actionability, persuasiveness, understandability) and on platform-specific safety characteristics (malicious-link exposure for web results, toxicity for forum threads, and five trauma-informed dimensions for AI systems: empathy and humanization, voice and choice, bias, risk-informed guidance, and support inform

Load-bearing premise

The paper assumes that questions extracted from r/Stalking — one self-selected, English-language community — represent the help-seeking needs of the broader TFA victim population, and that a single rubric can fairly compare long webpages, comment threads, and single-turn chatbot replies.

What would settle it

A reader could rerun the pipeline on queries from a different victim community — say a domestic-violence forum or a non-English support board — using the same rubric. If those queries yielded consistently safe, trauma-informed, and actionable guidance across all three channels, or if phishing-exposure and toxicity rates fell far below the reported 65% and 20%, the claim that online help-seeking systematically endangers TFA victims would be weakened. More narrowly, an independent re-annotation of the 90-pair webpage and 50-pair chatbot validation sets would test whether the cross-platform ranki

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • No single channel is safe to rely on: search covers the most queries but exposes roughly two-thirds of them to malicious links, forums provide community but almost no actionable or fully accurate guidance, and chatbots give the most actionable answers while still producing risky advice in about one in five responses.
  • Sound-sounding advice can be harmful in context: resetting a device, deleting an account, or blocking an abuser is standard security guidance but 'Damaging Guidance' for TFA victims because it destroys evidence and can escalate danger.
  • The least well-supported queries cut across the highest-consequence categories — financial platforms, people-search sites, image/video manipulation, and surveillance/tracking — so targeted work on these categories would address the weakest areas.
  • Because malicious-link exposure was consistent across all technology categories (63–69% of queries), the paper concludes no subgroup of victims is insulated from the risks of online help-seeking.
  • The authors attribute the survivor-chatbots' underperformance to design and evaluation shortcomings rather than missing domain knowledge, implying that safety-centered design is the binding constraint.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the paper is right, a natural next step is an intervention study: add safe-result filtering, evidence-preservation warnings, and crisis-resource inserts to search and chatbot outputs, then measure whether victims' protective actions improve.
  • The pipeline — survivor narratives to query corpus to unified safety grading — transfers to other help-seeking populations such as youth, immigrant survivors, or non-English speakers, who would likely need an expanded misuse taxonomy and locale-specific resources.
  • The headline rates are a snapshot of a fast-moving ecosystem; repeating the measurement periodically would show whether platforms are getting safer or merely changing the form of the harm.
  • One implication the authors leave implicit: victim-support organizations should treat search results and chatbot replies as part of the victim's risk environment — for example by publishing curated, pre-vetted search links and recommended prompts — rather than as neutral information.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 8 minor

Summary. The paper constructs a victim-centered dataset of technology-facilitated abuse (TFA) help-seeking queries by extracting questions from a decade of r/Stalking posts, classifying 11 technology-misuse categories, and simulating those queries across Google Search, existing Reddit comment threads, three general-purpose LLMs, and two domain-specific chatbots. Responses are evaluated on technical dimensions (relevance, accuracy, actionability, persuasiveness, understandability) and platform-specific social/safety dimensions (social-engineering risk, toxicity, empathy, voice & choice, bias, risk-informed guidance, support information). The headline finding is that Google Search and general-purpose LLMs provide considerably more relevant and actionable guidance than Reddit discussions, yet none of the systems consistently provide safe, trauma-informed support; the paper also reports that over 65% of queries encounter potentially malicious secondary URLs, over 20% of Reddit threads contain toxic comments, and domain-specific chatbots underperform general-purpose LLMs.

Significance. If valid, this is a timely and important contribution to the security, HCI, and victim-support literatures. The paper's strengths include a novel victim-authored query dataset, a multi-dimensional evaluation framework co-developed with social-work experts, explicit human validation of several automated classifiers, and public release of code, prompts, and a sample of processed data. The virus-total and Perspective-API measurements, the emphasis on evidence-destruction risks in technical advice, and the comparison of survivor-support chatbots against general-purpose LLMs are useful and falsifiable. However, the cross-platform comparison currently rests on a structural mismatch in how Reddit responses are paired with queries, and several core manually labeled metrics have low reported inter-rater reliability. These issues do not negate the value of the dataset or framework, but they do undermine the specific cross-platform ranking claims as currently stated.

major comments (1)
  1. [Throughout] The paper uses several thresholds and free parameters (e.g., PQCS τ=0.6, VirusTotal two-engine threshold, Perspective API 0.5, relevance validation sample size, understandability grade cutoff) without a sensitivity analysis. None of these is inherently wrong, but the headline percentages (65.5% malicious URLs, 52% Reddit relevance, 20% toxicity) are all threshold-dependent. A short sensitivity appendix showing how these figures vary with reasonable threshold changes would increase confidence in the conclusions.
minor comments (8)
  1. [Abstract and Section 4] The word 'reponses' appears in the full-text abstract; should be 'responses.' Also, the acknowledgments contain 'NationalbScience Foundation' — a typo for 'National Science Foundation.'
  2. [Figure 2b and Figure 13] Figure 2b's x-axis label contains Unicode/LaTeX artifacts ('Total/uni00A0Questions/uni00A0per/uni00A0Post'), and similar artifacts appear elsewhere. These should be cleaned before camera-ready.
  3. [Section 6.1.2] The heading misspells 'Cross-Platform' as 'Cross-Platfrom.'
  4. [Section 6.3.1] The sentence reporting 'κ=0.4, α=0.6' does not specify which annotation task these values refer to. The same sentence mentions two-coder and three-coder annotation; please attach the reliability statistic to the corresponding task and format.
  5. [Section 7.3.1 and Figure 9] For Empathy & Humanization and Voice & Choice, the paper reports 'the distribution of ratings across coders' rather than a single consensus label. The figure's stacked bars use 'Percentage of Coder Ratings'; this is acceptable, but the caption should state that the unit is coder ratings, not responses, to avoid confusion.
  6. [Section 4] The description of the Reddit response collection says that 2,476 of 2,797 queries had at least one associated comment. Since multiple queries can share the same post, the number of unique posts with comments is not reported. Please state both the number of posts and the number of queries, and explain how queries from the same post are treated in the evaluation.
  7. [Section 5.1] The Flesch–Kincaid Grade Level is used as the Understandability measure. The paper acknowledges this in Appendix F, but the main text should note that Flesch–Kincaid is a proxy and does not capture domain-specific jargon (e.g., 'spyware,' 'two-factor authentication'), which could be exactly what makes content inaccessible to victims.
  8. [Section 8] The Discussion states that domain-specific chatbots underperform general-purpose LLMs 'consistent with Prakash et al. [41]' — a 2026 reference. If this work is not yet published or is under review, please mark it as 'in press' or 'manuscript under review' so readers can judge the citation.

Circularity Check

0 steps flagged

No circularity: the paper is an empirical measurement study whose rubric, classifiers, and thresholds are externally anchored or manually validated; the Reddit query-alignment issue is a validity threat, not a derivation loop.

full rationale

The paper's chain is an empirical measurement pipeline rather than a formal derivation. Victim queries are extracted from r/Stalking posts and then used as inputs to Google Search, Reddit threads, and LLM/chatbot systems; quality is assessed with rubrics whose validation sets are human-annotated (e.g., relevance accuracy 87% for webpages/comments and 90% for LLM responses, Table 3). Technical metrics are adapted from external prior work [54]–[61], and social metrics are informed by external literature and consultation with social workers and victim advocates. No parameter is fitted to a subset of the evaluation outcomes and then renamed as a prediction. Thresholds such as VirusTotal >= 2 engines, Perspective > 0.5, and PQCS tau = 0.6 are stated as choices, not fitted to the headline comparisons. The authors cite some prior work from the same research group (e.g., [63]–[66], [74]–[76], [80]), but these citations support standard methodological conventions (toxicity thresholds, URL scanning practices, related analyses) and are not load-bearing for the central cross-platform claim. The skeptical concern that Reddit responses are the original comment threads rather than replies to the isolated extracted queries is a real external-validity and construct-matching limitation, but it is not circular: relevance is an empirical classification of thread content with respect to each query, and the finding that only 52% of Reddit queries received relevant responses could in principle have gone the other way; nothing in the evaluation definition forces the observed ranking by construction. The 'LLM-as-a-judge' components are validated against human consensus labels before scaling, so they do not reduce to the model's own outputs. No self-definitional step, fitted-input-called-prediction step, load-bearing self-citation, imported uniqueness theorem, ansatz-smuggling via citation, or renaming of a known result was found. Score 0.

Axiom & Free-Parameter Ledger

5 free parameters · 6 axioms · 0 invented entities

The paper introduces no new physical or conceptual entities; it uses an existing taxonomy of technology-misuse categories and a new dataset. The central claims rest on domain assumptions about representativeness, validity of proxy measures (LLM-as-a-judge, VirusTotal, Perspective API), and several hand-chosen thresholds. These are not circular, but they frame the interpretation of the empirical findings.

free parameters (5)
  • VirusTotal malicious-label threshold = ≥2 antivirus flags
    A URL is labeled malicious if at least two VirusTotal engines flag it. This threshold is chosen by hand following prior work and directly affects the 65.5% malicious-link finding (Section 7.1).
  • Perspective API toxicity threshold = >0.5
    Attribute scores above 0.5 are treated as positive for toxicity. This threshold affects the 'over 20% of Reddit discussions contain toxic responses' finding (Section 7.2).
  • PQCS similarity threshold τ = 0.6
    Threshold for retaining post sentences in the post-question context similarity metric, selected by manual calibration (Appendix D). It influences the choice of Llama3.3 over GPT-4 for question extraction, which shapes the downstream query dataset.
  • LLM evaluation sample size = 50 queries
    Only 50 of the 2,797 victim queries were used to evaluate LLMs/chatbots (250 QA pairs), for feasibility. All LLM/chatbot comparisons, including the underperformance of domain-specific chatbots, rest on this small sample (Section 4).
  • Understandability threshold = grades 9-12
    Responses at or below grades 9-12 on the Flesch-Kincaid scale are treated as understandable for general audiences; affects the interpretation of readability results (Appendix F).
axioms (6)
  • domain assumption r/Stalking is a representative source of TFA victim help-seeking narratives.
    The entire query dataset is derived from this single subreddit (Section 3 'Data Collection'), and all downstream findings depend on its representativeness.
  • domain assumption LLM-extracted questions faithfully preserve victims' information needs.
    Question extraction is validated indirectly via PQCS semantic similarity against posts, not against a gold standard of 'true' help-seeking questions (Section 3.2, Appendix D).
  • domain assumption LLM-as-a-judge, validated on small samples, is a valid proxy for large-scale human evaluation.
    Relevance, actionability, and persuasiveness are scored by an ensemble of three LLM judges validated on 90-100 manually annotated examples. The validation samples are small and inter-coder agreement is moderate (Section 6).
  • domain assumption Reference answers for technical accuracy are correct and complete.
    Technical accuracy is assessed against manually constructed reference answers grounded in authoritative sources; if the references are wrong or incomplete, all accuracy labels are affected (Section 6.2.1).
  • domain assumption A unified evaluation rubric can be applied comparably across webpages, comments, and chatbot responses.
    The framework (Section 5) applies the same technical metrics to long-form webpages, fragmented comment threads, and single-turn chatbot replies; differences in format may confound quality comparisons.
  • domain assumption VirusTotal and Perspective API provide valid indicators of maliciousness and toxicity.
    Social-engineering risk and toxicity are operationalized using these external tools (Sections 7.1 and 7.2); their false-positive/false-negative rates are not independently assessed here.

pith-pipeline@v1.3.0-alltime-deepseek · 27079 in / 14271 out tokens · 134544 ms · 2026-08-01T07:05:23.737765+00:00 · methodology

0 comments
read the original 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.

Figures

Figures reproduced from arXiv: 2607.21549 by Minjaal Raval, Mohit Singhal, Morgan PettyJohn, Nowshin Tabassum, Rachel Voth Schrag, Shirin Nilizadeh, Solomon G. Dandekar, Tim Ryan.

Figure 1
Figure 1. Figure 1: Overview of our Methodology Pipeline safety dimensions, enabling systematic comparison across platforms and categories of technology-facilitated abuse. Data Collection. We focus on the r/Stalking subred￾dit [43] because it is one of the few large, public com￾munities where technology misuse is embedded within real-world interpersonal abuse narratives and help-seeking discussions. We collected Reddit posts … view at source ↗
Figure 2
Figure 2. Figure 2: Distribution and nature of help-seeking questions [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cross-Platform Relevance QA pairs were sampled uniformly at random without strati￾fication by technology category. Two coders with computer science backgrounds independently annotated all pairs using the relevance rubric. Relevance was defined as whether a response contained technical information or advice that fully or partially addressed the victim’s question; factual and technical correctness were evalu… view at source ↗
Figure 4
Figure 4. Figure 4: Proportion of queries with at least one relevant response grouped by technology type [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy Evaluation when they provide vague advice or fail to identify concrete technical steps victims can take. 6.3.1. Evaluation Methodology. We evaluated Technical Actionability using the same LLM-as-a-Judge ensemble em￾ployed for Relevance (Section 6.1), comprising Llama 3.3, GPT-OSS 20B, and Gemma3:12b with majority voting. Each judge received the victim query, response, and Tech￾nical Actionability … view at source ↗
Figure 6
Figure 6. Figure 6: Actionability evaluation victim’s situation. We use two labels: Persuasive and Not Persuasive. A response is labeled Persuasive if it provides reasoning, justification, reassurance, or empowering lan￾guage supporting the recommended action, e.g “Enable two￾factor authentication; it prevents your ex from logging in even if they know your password” is Persuasive because it links the recommendation to a clear… view at source ↗
Figure 7
Figure 7. Figure 7: Persuasiveness Evaluation phishing malicious suspicious malware spam not recommended 0 20 40 60 80 100 Malicious URLs (%) 64.4 47.6 21.1 18.8 5.2 2.3 (a) Malicious URL Categories PROFANITY THREAT TOXICITY SEVERE_TOXICITY INSULT IDENTITY_ATTACK 0 20 40 60 80 100 Queries (%) 21.0 3.3 25.9 0.8 18.9 0.5 Has Toxicity No Toxicity (b) Type of toxicity [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Social Engineering in Google search websites (a) [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Social metrics evaluation of LLM responses. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example questions extracted by Llama3.3 and [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Examples of victim, abuser, TFA, and non-TFA queries extracted from Reddit posts. [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Co-occurrence of technology-misuse categories [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Accuracy of LLMs across tech-misuse categories. [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: CDF of Malicious Urls and Toxic Comments in [PITH_FULL_IMAGE:figures/full_fig_p019_15.png] view at source ↗

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Reference graph

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