AI slop accusations are public claims, made in online discussion, that a piece of writing was produced by a large language model rather than a human. The term comes from the dominant phrasing of the accusation itself ("that's AI slop"). In a 2026 working paper analyzing 25 million comments from Hacker News and Reddit, Jason Miklian and John E. Katsos document how this accusation register stabilized between 2023 and 2026, displaced older inauthenticity vocabulary such as shill and astroturf, and came to function primarily as social gatekeeping of perceived authenticity rather than as accurate detection of machine-generated text.
Between January 2023 and early 2026, the share of accusation-like comments carrying a pejorative AI label rose more than tenfold on both Hacker News and Reddit, while a placebo vocabulary of pre-2022 inauthenticity terms stayed flat or declined. The rise was therefore specific to AI accusation speech, ruling out a general increase in suspicious reading.
The vocabulary also consolidated. In 2023, pejorative accusations ran on a half-dozen frames such as drivel, garbage, and word salad. By 2026, the slop frame accounted for 94 percent of pejorative accusations. A population of readers converged on a single, short, mutually recognizable form, the lexical signature of what sociolinguists call enregisterment, compressed from the decades of classic cases into roughly three years.
The central empirical surprise of the research is a matched-control test. Prose features that statistically distinguish AI-generated text from human text, such as low contraction rates, formal-register adverbs, and high sentence-length variance, do not predict which human-written comments actually get accused. Accusers are not tracking the statistical signature of AI text. They rely on lay heuristics that are systematically miscalibrated.
The writers most exposed to false accusation are those whose prose is formal, polished, and low in contractions, regardless of how that prose was produced. An accusation says more about a community's epistemic state than about the accused text. This inverts the direction of harm in the AI epistemic injustice literature: lay AI literacy, operating in degraded epistemic conditions, produces testimonial injustice between humans at population scale.
Because the accusation's social fitness does not derive from detection accuracy, better AI detection tools will not fix the dynamic. Speech-act coding shows the register hardening over time, with mockery giving way to structural protest and institutionalized gatekeeping through moderator enforcement. Communities with strong prior norms of substantive engagement, such as r/changemyview, proved most resistant to accusation drift. The research argues the policy problem is one of epistemic infrastructure: AI literacy investment, credentialing mechanisms that survive the cheap-prose era, and discourse design that does not reward gatekeeping over engagement.
Miklian, Jason and John E. Katsos. "“That’s AI Slop, You Bot!”: Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments." arXiv preprint arXiv:2606.12073, 2026.
Miklian, Jason and John E. Katsos. "“That’s AI Slop, You Bot!”: Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments." arXiv preprint arXiv:2606.12073, 2026.
An AI slop accusation is a public claim, usually in an online comment, that a piece of writing was generated by AI rather than written by a human. Research by Miklian and Katsos (2026) analyzing 25 million comments shows that this accusation register rose more than tenfold between 2023 and 2026 and consolidated almost entirely around the word slop.
Generally not in the way accusers assume. A matched-control test found that the prose features that statistically distinguish AI text from human text do not predict which human comments get accused. Accusations track lay heuristics like polish and formality, not the actual statistical signature of AI writing, so formal human writers are routinely misidentified.
The evidence indicates that accusations serve social functions: boundary maintenance, status signaling, community gatekeeping, and protest against AI as a phenomenon. Speech-act coding shows mockery declining and structural protest and rule enforcement rising as the register matured, which means accusations increasingly work as social regulation rather than as detection.
No. Because the accusation's persistence comes from its social functions rather than its detection accuracy, improving detection technology adds nothing to the dynamics that sustain it. The research points instead to AI literacy, credentialing mechanisms, and discourse design as the levers that actually matter.