Does machine-generated text get penalized in online engagement?

The same paper asks what machine-generated text does in conversation, not how much of it there is. On style, MGT "conveys distinct social signals of warmth and status giving typical of language of AI assistants." The discussion says that across many subreddits, MGT "exhibits strong patterns of social support and status-giving, reflecting the characteristic style of modern AI assistants." On reception, the discussion says MGT comments "are often indistinguishable from HGT in terms of positive engagement, and in some cases even outperform them" (RQ4). The abstract is a little stronger, reporting engagement comparable to human-authored content and "in a few cases even higher."

The reasoning is that a visible stylistic difference does not carry an engagement penalty. The abstract reads this as evidence that AI-generated text "is becoming an organic component of online social discourse." The discussion goes further, arguing that GenAI "is not merely amplifying existing discourse but actively shaping new communicative norms." The excerpt does not say how warmth or status were coded: it names no lexicon, classifier or annotation procedure for social signals. It also leaves engagement undefined. The paper says MGT "achieves engagement levels" without saying whether that means votes, replies or another measure. No effect sizes or tests appear in the excerpt.

This sits against the companion-behavior result in Do chatbot companionship behaviors actually increase how much people like them?. There, annotators rated chatbots less likable, humanlike and trustworthy when conversations showed companionship behaviors. Here, warm, status-giving text posted on Reddit draws engagement comparable to human text. The two use different measures, annotator ratings of simulated conversations against engagement with real posts, so they are a scope contrast rather than a refutation. A reading that fits both, though neither excerpt tests it, is that the same register is discounted when judged as a chatbot and rewarded when it appears in a crowd. The trust side is open as well. Does chatbot language style actually shape how much we trust it? treats expertise signals as shaping trust. This excerpt measures engagement, not trust, so it cannot say whether the engagement it finds comes with any.

The excerpt does not establish whether the engagement comes from the wording itself. MGT is concentrated in a few users, and the excerpt does not say whether those users post in different threads or whether engagement is adjusted for subreddit, author or topic. It gives no mechanism for why warm, status-giving language draws as much engagement as human text, and it says nothing about whether readers can tell the two apart. At this strength the finding is a co-occurrence in the communities sampled: assistant-style text is not penalized there in aggregate. It is not evidence that readers prefer machine text, or that the engagement reflects its quality.