Do AI slop accusations actually detect AI text?

The paper's central claim is that "AI slop" accusations have settled into a stable register on Hacker News and Reddit, and that the register works as social gatekeeping rather than as a detector of AI writing. The corpus is about 25 million comments from Hacker News and 18 sampled subreddits, January 2023 to May 2026. The authors report 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," and that the slop frame "now constitutes 94 percent of pejorative mentions." The decisive result is a matched-control test of accused against non-accused parent comments, which found that "prose features that statistically distinguish AI from human text do not predict which human text gets accused as AI."

The mechanism is signaling theory run in reverse. When a trusted signal collapses (here, good prose as a marker of effort), populations coordinate on "a substitute that is harder to fake." The authors argue the substitute stabilizes through enregisterment. A reply such as "This is AI slop, get this off the sub" evaluates the writing, positions the speaker against the writer and claims community membership in one move, and the accumulated stance-taking becomes a recognized register. The substitute "survived instead on other grounds": it "was generally effective as a gatekeeper for any speech that a commenter wanted silenced." The placebo, affective-hardening and cross-platform checks are offered to rule out generalized suspicion, a decomposing register and platform-specific drift.

Within the library, this is the reader-side counterpart to the detection problem. The fake-news detector note finds that classifiers flag LLM text because of its linguistic patterns; this paper finds that lay accusers are not reading those same patterns, so the label is not even tracking the signal a classifier would use. It also gives the cultural-posture note an empirical case. An accusation register is one candidate posture toward AI discourse, and on the paper's evidence it does not discount AI-written text accurately. The cheap cue standing in for a judgment readers cannot make parallels Does polished AI output trick audiences into trusting it?. The paper sets its reader-side focus against production-side accounts that "treat the writer as the locus of action," the side on which Does AI writing assistance change how readers perceive the writer? reports its distortion.

The excerpt does not say which prose features were tested or how the matched controls were built, so the "do not predict" result cannot be checked in its specifics here. The accusation labels come from a 137-pattern regex lexicon followed by per-comment LLM judgments (Claude Opus 4.7) on stratified samples of 5,000 Reddit and 2,500 Hacker News comments. No precision or agreement figures are given, and the lexicon was tuned through a false-positive audit on a single subreddit. The 94 percent figure carries no date window. What the evidence supports is that these accusations work more as social regulation than as screening. That better detection cannot fix this is the paper's argument, not something its data test.