"That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments

“That’s AI Slop, You Bot!”: Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking. How have readers responded, and what can this tell us about changing anti-AI attitudes? We analyzed 25 million comments from Hacker News and Reddit (2023-2026), combining LLM judgment on 7,500 sampled accusations of AI use, sentiment trajectories, speech-act coding of 300 confirmed accusations of AI use, and a matched-control test of accused versus non-accused parent comments. We found that the pejorative-label share of accusations rose more than tenfold on both platforms while a placebo vocabulary of pre-2022 inauthenticity terms (“shill”, “astroturf”) did not. This shift reflected a fast-growing trend of branding any suspicious or seemingly inauthentic prose as “AI slop”. The slop frame now constitutes 94 percent of pejorative mentions, with the dominant comments shifting in tone from mockery toward gatekeeping and structural protest. The key surprise comes from a matched-control test which found that prose features that statistically distinguish AI from human text do not predict which human text gets accused as AI. The new accusations work as social gatekeeping of perceived authenticity without actually screening for AI. This research extends signaling theory by showing that substitute signals used socially can grow even when inaccurate if the underlying detection problem cannot be solved at the non-expert level. It shows that AI's effects on writing from the reader side are distinct from those on the production (writer) side. Detection technology cannot resolve this dynamic because the social function of accusations is increasingly to perform social gatekeeping and in-group signaling as opposed to identifying AI-generated writing.

Keywords: generative AI, signaling theory, social epistemology, metalinguistic policing, online communities

Introduction. The arrival of generative large language models (LLMs) has both cheapened the production and amplified the proliferation of fluent prose. Most work on AI and writing has tracked impacts on the producer side, asking whether writers changed their prose in response to AI tools, or if workers are hired less often because content no longer indicates worker quality reliably (Galdin & Silbert 2025). With the proliferation of Generative AI, the signal "good prose means real effort, thinking, and expertise" became cheap to fake at scale, with deep consequences for AI-influenced content markets (Howell & Potgieter 2025; Tullis 2025) and for the credibility of research that depends on written signals (Obschonka & Lévesque 2026).

Existing accounts of AI and writing largely treat the writer as the locus of action through avoidance, adoption, or hybridization. But how do consumers (readers) feel about this change? What can be learned by studying the actions of commenters who impose a cost on stigmatized features through accusation speech?

The article proceeds as follows. Section 2 develops the theoretical scaffolding. Employing signal theory, we ask whether and how consumers organize around harder to fake properties. We examine the possibility that AI use accusations that are unfounded might invert the direction of harm, where the model has been positioned as a perpetrator of testimonial injustice (Kay et al. 2024). Here, lay AI (il)literacy would be the agent, and the writer/producer is the target. If the social function of accusations of AI use is, in fact, gatekeeping rather than the identification of AIgenerated writing, detection-improving technology may exacerbate the problem it intends to fix.

Section 3 describes our data and methods, and Section 4 reports the empirical findings that the trajectory has the shape that signaling theory predicts, namely, that when a trusted signal collapses, populations coordinate on a substitute signal that is harder to fake. The “slop” accusation has that shape: short, recognizable, costly to deploy sincerely, cheap for others to reproduce. We argue that debates about AI writing have focused too narrowly on production while overlooking how readers respond. Section 5 discusses the implications of the findings, suggesting that accusations like “AI slop” spread because they serve social functions such as boundary maintenance, status signaling, and gatekeeping rather than because they accurately detect AIgenerated text. In other words, accusations are less a screening tool than a form of social regulation.

Related work. 2. Theoretical framing The standard prediction from signal theory would be that when exposed to LLM-heavy material, readers will coordinate on a more authentic substitute whose properties make it harder to fake than the original. Given that markets with information asymmetry between buyers and sellers tend toward degraded equilibria where bad-quality producers dominate (Akerlof 1970), and costly signals like credentials or warranties can re-coordinate buyers and sellers around a substitute that is harder to fake (Spence 1973), two questions follow from this framework.

The mechanism by which a substitute stabilizes in a population in the context of online writing is sociolinguistic. Enregisterment theory describes how a set of features is recognized as a coherent variety indexing particular speakers or stances (Agha 2003, 2007), extended to cases ranging from Pittsburghese to Netspeak on timescales of decades or longer (Silverstein 2003; Johnstone et al. 2006; Squires 2010). A complementary account describes how ordinary speakers participate in defining language through cumulative metalinguistic speech acts (Cameron 1995; Lukač & Heyd 2023). Stance theory describes the individual speech-act mechanism (Du Bois 2007; Bucholtz & Hall 2005). When a Reddit user replies to a post, "This is AI slop, get this off the sub," the speech act evaluates the writing, positions the speaker against the writer, and claims community membership and gatekeeping in one short move. Cumulative stance-taking by community members aggregates into what is called enregisterment.

The features of major platforms such as textual persistence, large audiences, ranking, and low cost of uptake accelerate the cycle from the multi-decade timescale of classical cases to whatever timescale the users engage on. The rapid adoption of explicit subreddit-level AI rules between mid-2023 and late 2024 (Lloyd et al. 2025) raises the possibility that accusation stabilization is an artifact of top-down rulemaking rather than cumulative stance-taking. If the accusation trajectory runs parallel on platforms with and without such rules, the finding supports a grassroots-prescriptivism account over a governance-driven one. This generates two further research questions.

Moreover, epistemic injustice is harm when a speaker is wronged in their capacity as a knower (Fricker 2007). Recent work has extended the framework to AI contexts with the model positioned as a perpetrator of injustice (Kay et al. 2024) and AI systems theorized as artificial epistemic authorities (Hauswald 2025). However, humans can wrongly attribute AI authorship to other humans. AI-mediated communication forces a choice between gullibility and blanket distrust (Sahebi & Formosa 2025).

Method. 3. Data and methods Our primary data set comprises all public comments from Hacker News (HN) and 18 sampled subreddits between 1 January 2023 and 15 May 2026. Total scanned volume across the panel is c. 25 million comments, 12 million from HN and 13 million from Reddit. Reddit data was pulled from the Arctic Shift archive via the public JSON API. Hacker News data was pulled from the Algolia Hacker News search archive. Subreddit selection captures variation across four community types: AI-focused communities (r/aiwars, r/ArtistHate, r/ChatGPT, r/OpenAI, r/ MachineLearning, r/LocalLLaMA, r/singularity), creative communities (r/Art, r/writing, r/books), general discourse communities (r/AskReddit, r/news, r/changemyview, r/explainlikeimfive, r/ AskHistorians, r/science), and tech and academic communities (r/programming, r/AskAcademia). The sampling fraction is held constant across months for these subs, which preserves within-sub time-series comparability.

Candidate comments were identified using a 137-pattern regex lexicon organized into five tiers. Tier 1 (“Direct”) captures direct accusations such as "ChatGPT wrote this," "is this AIgenerated," and "OP is a bot." Tier 2 (“Pejorative”) captures pejorative labels: AI slop, GPT garbage, ML drivel, robo-writing, and related cognates, with bare "slop" requiring an AI-context check. Tier 3 (“Style”) captures stylistic-tell callouts including em-dash mentions, the "delve" callout, tricolon mentions, and the broader vocabulary of "classic AI signature." Tier 4 (“Mocking”) captures mock and parody patterns matching canonical AI-assistant phrases ("fellow humans," "in the rapidly evolving landscape," "rich tapestry"). Tier 5 (“Indirect”) captures indirect sense-based identification ("smells like AI," "reads like ChatGPT," "uncanny valley of writing"). High-falsepositive patterns in Tiers 3 and 4, such as "worth noting," "it's important to note," and "is this a human," require an AI-context token within 250 characters of the match. The lexicon and full audit notes are available in the supplementary materials (Online Resource 1 and Online Resource 2).

To correct for the rule classifier's tier-level imprecision, two stratified per-comment LLM judgment passes were run with Claude Opus 4.7. The Reddit pass drew a 5,000-comment sample with 1,000 candidates per tier, balanced across year and sub-cluster strata. The Hacker News pass drew a separate 2,500-comment sample with 500 candidates per tier, balanced across the 41 months. Each comment in both samples was classified by per-comment LLM judgment into one of five categories. REAL covers genuine AI accusations including pejorative framing, direct callouts, tell-callouts, and accusatory questions. DISCLOSURE covers comments whose text is itself AIgenerated or self-identifies as AI. NEUTRAL-REF covers non-accusatory references to AI, including on-topic discussion in AI subs and user-reported AI use. FP covers regex false positives where the pattern fired on ordinary text. AMBIGUOUS covers cases that cannot be decided from the comment alone.

Affective hardening was tested through three independent procedures. First, the proportion of Tier 2 hits whose body text contained the slop frame, the older derogatory frame set (drivel, garbage, trash, vomit, sludge, mush, gunk, junk, crap, word salad, nonsense), both, or neither was computed by month across all 736 Reddit sub-months. Second, the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment compound score was computed for every LLMvalidated REAL accusation on Reddit, aggregated to month and to tier. Third, a 300-thread stratified sample of LLM-validated REAL Reddit accusations was qualitatively coded into one of five speech-act types: SNEER (mocking dismissal), DISMISS (curt rejection), MOCKERY (parodic imitation), GATEKEEP (community-membership claim or rule-enforcement), and STRUCTURAL_PROTEST (objection to AI use as a general phenomenon rather than to the specific comment).

Discussion. Our findings show that, when generative AI degraded the signal value of good prose as a marker of effort, populations of online forum readers coordinated on a substitute accusation register. These signals stabilized through enregisterment but did not acquire the detection accuracy that classical signaling theory would have predicted as the condition for the substitute’s survival. The substitute survived instead on other grounds, namely that it was generally effective as a gatekeeper for any speech that a commenter wanted silenced (for whatever reason). The placebo design rules out generalized suspicion, while the three measures of affective hardening rule out the possibility that the register is decomposing, and the cross-platform parallel structure rules out platform-specific drift. To our knowledge, this is the first observational empirical finding that AI-use accusations are not made primarily to identify the use of AI.

The persistent 2-4 percentage point lead Hacker News carries over Reddit across all 41 months is not incidental. HN’s user base is disproportionately composed of software engineers, researchers, and early adopters who encountered LLMs before the general public. These users thus had both greater prior exposure to AI-generated text and stronger prior motivation to identify and police it. The gap is consistent with a diffusion model in which technically fluent communities develop and stabilize accusation norms earlier, with those norms spreading to broader-audience platforms subsequently. Reddit’s convergence on nearly the same share by 2026 confirms that the register is not platform-specific and the HN lead likely reflects timing.

The implication for signaling theory concerns the receiving side of an unpriced discourse. The sending side of the signal collapsed under generative AI, a phenomenon that has been modeled in a priced labor market (Galdin & Silbert 2025). Under conditions where the underlying detection problem cannot be solved at the lay level, substitute signals can stabilize on social purpose alone without acquiring technical accuracy. The purpose comes from the social functions the accusation performs, each of which gives selection pressure that does not depend on whether the accusations track the actual statistical likelihood for AI use. The conditions under which such equilibria can be expected to form are common in domains touched by generative AI. The empirical map provided here is a starting point for theorizing them. Our results here would predict that similar AI-use accusations will form for image authentication, voice authentication, and code authorship among others, with the core intent of the lay accusation being gatekeeping rather than empirically accurate detection of AI use. This will become increasingly problematic as AI in those areas reduces even the empirically detectable tip-offs that experts can find. This could have the effect of increasing the role of experts in verifying AI vs. non-AI content; or it could greatly reduce trust in any type of medium that can be plausibly generated by AI.

Our findings also invert the potential direction of harm through the recent literature on epistemic injustice and AI. Existing extensions of Fricker's framework position the AI system as a perpetrator of testimonial injustice, with harm flowing from machine to human (Kay et al. 2024), while adjacent work theorizes that AI systems are artificial epistemic authorities (Hauswald 2025). Our findings flow in the opposite direction, suggesting that lay AI literacy, operating in degraded epistemic conditions, produces testimonial injustice between humans at population scale, in this case directed towards writers by readers.

Conclusion. The post-ChatGPT period produced a test of what populations do when a signal for quality collapses and the underlying detection problem cannot be solved at the lay level. Between 2023 and 2026, readers on Hacker News and Reddit coordinated on a substitute screening signal. The substitute stabilized without acquiring detection accuracy, and its social functions supplied the fitness that classical signaling theory would have expected to come from improved screening. The matched-control test makes the miscalibration explicit, and the placebo, cross-platform, and affective-hardening checks rule out the principal alternative explanations.

The findings have implications across three literatures. For signaling theory, they describe a class of equilibria in markets with information asymmetry where substitute signals stabilize on social fitness without acquiring screening accuracy. For social epistemology, they document an inverted form of testimonial injustice in which lay AI literacy produces harm to human writers. For the sociology of cultural production, they supply the empirical anchor that boundary-work readings of AI shaming have lacked. Future work needs to take the receiving side seriously as the primary site of analysis, with production effects as downstream consequences of policing intensity. The empirical map provided here shows how that cost has shifted and where it concentrates.

Limitations. The current study has several limitations. The lexicon was tuned through one iterative falsepositive audit on r/explainlikeimfive; other subs may carry idiosyncratic false-positive patterns we have not surfaced, partially mitigated by per-tier LLM precision. Platform coverage stops at Hacker News and Reddit. A pilot attempt at Stack Exchange was deferred because of access constraints, yet Stack Exchange remains a useful comparison given its formal-register expectations and active moderation. Cross-language coverage is absent, and this limitation matters more here than in comparable studies. The English-language accusation register documented here is lexically specific: “slop” carries connotations of organic waste and low-quality mass production that do not translate straightforwardly. Whether equivalent registers have stabilized in French, German, Mandarin, Arabic or other online communities, and whether non-English communities converge on a single dominant pejorative as English did (or remain more fragmented across competing terms) is an open empirical question. The answer would test whether the lexical consolidation dynamic is a general feature of online community behavior under generative AI pressure or an artifact of Englishlanguage platform demographics.