{"id":"31b39476-b7f7-41e4-b5c8-61b83dee5fe5","arxiv_id":"2509.08912","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"Across seven AI chatbots, Reddit users report mostly reliability failures, with each chatbot showing a distinct pattern of safety, privacy, and security complaints.","lead":"This study mines Reddit comments about seven major AI chatbots and sorts complaints into seven official AI risk categories. It finds that each chatbot has a distinct risk fingerprint, with performance errors dominating everywhere, while safety, privacy, and security worries vary by product.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Risk-fingerprint claim may rest on pseudo-replication: chi-square residuals (Table 2) treat 32,302 risk mentions as independent even though mentions are nested in posts, threads, and product-specific subreddits.","rationale":"I read the paper as an empirical HCI claim: Reddit users' risk reports are unevenly distributed and each major LLM has a distinct NIST risk fingerprint. The load-bearing evidence is the quantitative comparison in Section 4.2, because the abstract's headline claims (GPT-Safe/Fair, Gemini-Privacy, Claude-Secure, Qwen-Valid) are stated in terms of these residual patterns. The strongest threat is not demographic representativeness per se (which the authors concede in Section 7), but the pseudo-replication in the unit of analysis. With 32,302 mentions from only 4,438 posts, the effective sample size is much smaller than N=32,302, and product-specific subreddits make the clustering align with product labels. A chi-square test on raw mentions will report statistical significance for even trivial differences, and adjusted residuals are inflated. This is a correctable, falsifiable concern rather than a fundamental design flaw. The qualitative quotes in Section 5 provide real lived-experience evidence, and the annotation pipeline validation (alpha=0.76/0.82, F1=0.83) is a strength; I am not claiming fabrication or intentional bias. But until the residuals are re-estimated with clustering, the specific fingerprint rankings should be treated as directional at best. This supports the reader's CONDITIONAL verdict; I do not see grounds to reject the paper.","tokens_in":29443,"tokens_out":6238,"duration_ms":67003,"concrete_test":"Re-run the Section 4.2 analysis at the post/thread level: for each post (or randomly selected comment per thread), compute product-category counts and recompute the chi-square residuals in Table 2; alternatively, bootstrap by post/thread with 10,000 resamples to obtain confidence intervals for the residuals. If the four headline residuals (GPT-Safe, Gemini-Privacy, Claude-Secure&Resilient, Qwen-Valid&Reliable) fall below |r|=2 or their confidence intervals include zero, the fingerprint claim is not supported by the present data.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central 'risk fingerprint' claim depends on the chi-square post-hoc analysis in Section 4.2/Table 2. The unit is the individual extracted risk mention (32,302 relations in Section 4.1), but these mentions are not independent. They come from a corpus of 4,438 posts and 48,797 comments (Section 3.1), many comments belong to the same thread, and the sampling design deliberately includes product-specific subreddits (r/ChatGPT, r/ClaudeAI, r/Qwen_AI; Table 1). A viral thread about one Gemini privacy incident, or the normal topic focus of r/Qwen_AI on model quality, can generate many correlated mentions in one cell. The chi-square test and the adjusted residuals (|r|≥2) ignore this clustering, so the reported residuals and p-values are anti-conservative; 'unique fingerprints' could reflect subreddit topic norms rather than product-level user risk experiences. Section 7 acknowledges PRAW's top-post/popularity limits but does not address the nested-data issue in the statistical test.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents a large-scale, mixed-methods study of user-reported risks with seven LLM chatbots by analyzing Reddit posts and comments from November 2022 through June 2025. The authors build an LLM-driven extraction pipeline, grounded in the NIST AI Risk Management Framework's seven risk characteristics, to tag 32,302 risk mentions across 4,438 posts and 48,797 comments. They combine this with BERTopic-based bottom-up clustering, an interactive knowledge graph, chi-square post-hoc residual analysis, and qualitative thematic analysis. The main claims are that user-reported risks are unevenly distributed across chatbots, that 'Valid and Reliable' dominates at 58.39% of tagged mentions, that each chatbot has a distinctive 'risk fingerprint' (e.g., GPT with Safe and Fair, Gemini with Privacy, Claude with Secure and Resilient), and that lower-frequency risks are experienced as trade-offs while higher-frequency risks are experienced as direct harms. The paper contributes an empirical mapping of user-perceived LLM risks and argues for user-centered AI risk management.","tokens_in":29697,"tokens_out":6583,"duration_ms":81621,"significance":"If the empirical claims hold, this is a timely and useful contribution to HCI and trustworthy-AI research. The paper addresses a genuine gap: most risk taxonomies are system-centered and lab-based, whereas this study draws on unsolicited user discourse across multiple chatbot products. The authors are appropriately transparent about their pipeline: they validate the GPT-4.1-mini annotation on 200 samples (NIST-category accuracy 0.87, Krippendorff's alpha 0.76, risk-type F1 0.83), make the knowledge graph publicly available, and provide detailed appendices with thematic codebooks. The qualitative quotes and thematic groupings give the findings concreteness. The main risk is that the central comparative claim—the 'risk fingerprint'—rests on a statistical analysis that does not account for the nested, non-independent structure of the data; this is fixable, but until addressed the fingerprints should be treated as provisional.","major_comments":[{"comment":"The chi-square post-hoc analysis treats each of the 32,302 extracted risk mentions as an independent observation, but these mentions are nested within comments, threads, posts, and product-specific subreddits. The corpus has only 4,438 posts and 48,797 comments (Section 3.1); a single viral thread or the normal topic focus of r/Qwen_AI can generate many correlated mentions in one cell. The adjusted residuals and p-values are therefore anti-conservative, and the 'risk fingerprints' may reflect corpus structure rather than product-level user experience. Please re-analyze at the post or comment level (e.g., mixed-effects regression with random intercepts for thread and subreddit, or cluster-bootstrapped residuals) and report whether the Table 2 pattern survives.","section":"Section 4.2, Table 2"},{"comment":"The sampling design uses PRAW top posts from 51 hand-selected subreddits, including product-specific communities such as r/ChatGPT, r/ClaudeAI, and r/Qwen_AI. This creates a plausible confound: differences in subreddit norms and topics can produce cross-chatbot differences in risk mentions even if the products themselves do not differ. Section 7 acknowledges the top-post popularity bias but does not address subreddit confounding. Please add a sensitivity analysis excluding product-specific subreddits or controlling for subreddit membership; at minimum, quantify how much of the chi-square association is driven by these communities.","section":"Section 3.1, Table 1"},{"comment":"The annotation pipeline uses GPT-4.1-mini, an OpenAI model, to classify every risk mention, including mentions about OpenAI's ChatGPT. Validation is reported only in aggregate (accuracy 0.87, alpha 0.76, risk-type F1 0.83). Given the highly imbalanced category distribution and the possibility of systematic labeling tendencies by the annotator model, aggregate metrics do not rule out differential annotation bias across chatbots or across NIST categories. Please report per-category precision/recall and, ideally, per-chatbot validation, or re-annotate a sample with a different model to assess whether the Table 2 fingerprints are stable across annotators.","section":"Section 3.2"},{"comment":"The abstract and Section 6 claim that lower-prevalence risks (Explainability, Privacy) manifest as trade-offs while higher-prevalence risks (Fairness, Safety) are experienced as direct personal harms. This is a central empirical claim, but it is supported by a purposively sampled qualitative narrative rather than a systematic coding or analysis. Several categories contain both framings: Fairness includes both direct stereotyping harm and an academic-dishonesty social dilemma, and Privacy includes coerced trade-offs. If this is a substantive prevalence-dependent claim, it needs an operationalized comparison; otherwise it should be presented as an interpretive hypothesis rather than a finding.","section":"Sections 5 and 6.3"}],"minor_comments":[{"comment":"'Platform-specific' is ambiguous because the data come from a single platform (Reddit). Consider 'product-specific' or 'chatbot-specific.'","section":"Abstract"},{"comment":"The text says |r| ≥ 2 while Table 2's note says |r| > 1.96; align these. Also report the chi-square statistic, degrees of freedom, and p-value, not only the post-hoc residuals.","section":"Section 4.2, Table 2"},{"comment":"The paper reports an average of 61.3% for 'Valid and Reliable' in Section 4.2 but 58.39% of total tagged mentions in Section 5.7 and Table 12. Clarify that the former is an unweighted mean across chatbot percentages and the latter is the overall share.","section":"Section 4.2 / Section 5.7"},{"comment":"The numeric labels under each column are not explained in the caption or text. Add a legend or caption describing what these values represent.","section":"Figure 4"},{"comment":"Several cells contain only 'high report volume' without the corresponding frequencies or residuals. Add exact values or point readers to Table 2.","section":"Table 3"},{"comment":"The Fair example contains a typo ('tras‘' instead of 'trash'). Also, 'Deepseek' in the table should be 'DeepSeek' for consistency.","section":"Table 4"},{"comment":"The introduction refers to 'ARF' but the rest of the paper uses 'AI RMF'; define and use one abbreviation consistently.","section":"Section 1"}],"recommendation":"major_revision","confidential_remarks":"This is a well-positioned HCI paper with a valuable corpus and a transparent annotation pipeline. The main issue is statistical: the 'risk fingerprint' claim is built on a chi-square analysis that ignores the nested structure of the Reddit data, so the current evidence is not as strong as the framing suggests. I would encourage the editor to request a clustered or post-level re-analysis, or a softening of the claim if such analysis is not possible. The note about subreddit confounding and annotator-model validation should be addressed in the revision. The author self-citations are not load-bearing, but the list could be trimmed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a genuinely useful descriptive map of what Reddit users complain about across seven chatbots, and the first cross-chatbot comparison I know of using a standard risk taxonomy. But the headline 'risk fingerprint' claim is built on a chi-square test that treats each extracted risk mention as independent, and they're not. My read: the descriptive percentages and themes are worth taking seriously; the fingerprints are directional at best.\n\nWhat's new: prior work mostly focuses on ChatGPT or a single risk. Here they analyze posts/comments across GPT, Claude, Gemini, DeepSeek, Llama, Mistral, Qwen, mapping to NIST AI RMF categories, and produce a knowledge graph plus qualitative themes. They validate the LLM annotation pipeline on 200 consensus-coded samples: accuracy 0.87 for NIST category, alpha 0.76, F1 0.83 for risk type. That's a real effort, and the qualitative quotes anchor the numbers in lived experience.\n\nSoft spots: three, roughly in decreasing order. First, the chi-square post-hoc (Table 2) ignores nesting. 32,302 mentions come from 4,438 posts and 48,797 comments, many in the same thread and product-specific subreddits. A single viral thread can produce many correlated mentions in one cell. So the 'unique fingerprint' could partly reflect what each subreddit talks about, not what users generally experience. The authors call the residuals 'directional diagnostics,' which is honest, but the abstract and discussion present fingerprints more firmly than the stats support. Second, the corpus is top posts from hand-selected subreddits via PRAW; Section 7 concedes popularity/recency bias but doesn't address clustering. Third, no data or code released, so independent verification is limited. The annotation model is a GPT variant from one of the studied product families, though the human validation mitigates that concern.\n\nBottom line: this is a solid exploratory HCI study, not a definitive risk census. The risk hierarchy (Valid and Reliable dominant) is probably robust; the per-product fingerprints are a hypothesis worth testing with a random sample or a multilevel model. I'd send it to peer review, ideally to a CHI-adjacent venue with people who will catch the pseudo-replication issue. I'd cite it and bring it to a reading group.","headline":"A useful descriptive map of Reddit-reported risks across seven chatbots, but the per-product 'risk fingerprints' rest on a chi-square test that ignores nested data.","tokens_in":30185,"tokens_out":2632,"would_cite":true,"duration_ms":28162,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Reddit users report that LLM failures are the biggest risk, and each chatbot has a distinct risk fingerprint","keywords":["LLM risks","Reddit","NIST AI RMF","user-reported risks","risk fingerprint","knowledge graph","trustworthy AI","HCI"],"falsifier":"A direct test would be to run the same annotation pipeline on a different data source, such as app store reviews or a nationally representative survey of LLM users, and check whether 'Valid and Reliable' still dominates and whether the per-chatbot risk fingerprints (GPT safety, Gemini privacy, Claude security, Qwen reliability) replicate. If those profiles do not generalize, the paper's central claim fails.","tokens_in":29323,"feed_emoji":"💬","tokens_out":1083,"duration_ms":14202,"temperature":0.7,"pith_summary":"This paper analyzes user-reported risks of seven major LLM chatbots by mining Reddit discussions and mapping them onto the U.S. NIST AI Risk Management Framework. It finds that 'Valid and Reliable' failures dominate user complaints across all chatbots, but each product shows a unique secondary risk profile: GPT is associated with safety and fairness issues, Gemini with privacy, Claude with security and resilience, and open-source models like Qwen concentrate almost entirely on reliability. The paper argues that this user-centered view reveals a gap between the risks emphasized in technical research and the risks users actually experience, and that users cope through pragmatic, coerced, and voluntary trade-offs.","feed_headline":"Reddit users say LLM reliability is the top risk","feed_subtitle":"An analysis of 48,000 comments reveals each chatbot has a unique risk fingerprint, from GPT safety to Gemini privacy.","key_machinery":"The knowledge graph (KG) that connects LLM chatbots to NIST AI RMF categories and to granular risk types extracted via an LLM pipeline, visualized with D3.js. The KG serves as the analytical backbone, combining a top-down framework (NIST categories) with bottom-up topic modeling (BERTopic) to quantify risk prevalence and link it to qualitative user quotes.","core_discovery":"The central claim is that user-reported risks of LLM chatbots in the wild are unevenly distributed and platform-specific. Using a knowledge graph built from 4,438 Reddit posts and 48,797 comments, the authors show that 'Valid and Reliable' risk is the most frequently mentioned category (58.39% of tagged risk mentions), while less frequent risks like 'Explainability' and 'Privacy' manifest as nuanced user trade-offs and more common risks like 'Fairness' are experienced as direct personal harms. Each of the seven chatbots exhibits a distinct 'risk fingerprint': GPT is overrepresented in 'Safe' and 'Fair' issues, Gemini in 'Privacy', Claude in 'Secure and Resilient', and Qwen almost entirely in","pith_inferences":["A direct extension of the risk-fingerprint idea is that platform-specific design choices (e.g., GPT's safety filters, Gemini's data practices, Claude's stability) causally shape which risks surface, not just which risks users happen to discuss.","The method of using an LLM to annotate LLM-related risks could introduce a self-confirming bias; a human-only coding on a larger sample would test whether the fingerprints persist.","The 'pragmatic, coerced, voluntary' trade-off taxonomy could be applied to other AI products (e.g., voice assistants, recommender systems) to predict when users will tolerate failures.","If Reddit's user base skews technical, the dominance of 'Valid and Reliable' might be inflated; a cross-platform replication (e.g., Twitter/X, Facebook, or app store reviews) would reveal whether the risk hierarchy is an artifact of the platform."],"forward_implications":["If the paper is right, designers and policymakers should prioritize improving reliability and validity of LLM outputs over other risk categories, since that is what users report most.","Different chatbots have different risk profiles, so risk mitigation should be tailored per product rather than applied uniformly.","Users' lived experiences reveal that some risks are accepted as trade-offs, meaning safety and transparency interventions must respect users' pragmatic and voluntary choices.","The observed gap between system-centered and user-centered risk priorities suggests that technical risk research may be addressing issues that are less salient to everyday users.","The prevalence-based distinction between direct harms and trade-offs offers a way to triage which risks require immediate intervention versus which are matters of user preference."],"fun_headline_variants":["Each LLM chatbot has a unique risk fingerprint","Reddit users say reliability is the top LLM risk","LLM risk profiles differ: GPT safety, Gemini privacy, Claude resilience","Rare LLM risks are trade-offs, common ones are personal harms"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The paper assumes that the Reddit discussions scraped from 51 hand-selected subreddits, filtered by post popularity and comment length, are representative of what typical LLM users experience in the wild, rather than being skewed by Reddit demographics, subreddit norms, or the platform's top-post retrieval bias.","fun_headline_variants_meta":{"raw":{"variants":["Each LLM chatbot has a unique risk fingerprint","Reddit users say reliability is the top LLM risk","LLM risk profiles differ: GPT safety, Gemini privacy, Claude resilience","Rare LLM risks are trade-offs, common ones are personal harms"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000571,"raw_usage":{"total_tokens":2552,"prompt_tokens":773,"completion_tokens":1779,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":517,"completion_tokens_details":{"reasoning_tokens":1708}},"tokens_in":517,"tokens_out":1779,"duration_ms":16557,"temperature":1.0,"reasoning_tokens":1708,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T19:59:20.637765+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test would be to run the same annotation pipeline on a different data source, such as app store reviews or a nationally representative survey of LLM users, and check whether 'Valid and Reliable' still dominates and whether the per-chatbot risk fingerprints (GPT safety, Gemini privacy, Claude security, Qwen reliability) replicate. If those profiles do not generalize, the paper's central claim fails.","supporting_citations":[],"review_version":1}