Evidence map›Paper›PMID 41675465›Full record

ArticlePNAS nexus2026

Labeling messages as AI-generated does not reduce their persuasive effects.

Isabel O Gallegos, Chen Shani, Weiyan Shi, Federico Bianchi, Izzy Gainsburg, Dan Jurafsky, Robb Willer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Isabel O GallegosDepartment of Computer Science, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-4872-6447
Chen ShaniDepartment of Computer Science, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0009-0001-4902-616X
Weiyan ShiDepartment of Electrical and Computer Engineering and Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.ORCID https://orcid.org/0000-0003-0850-5831
Federico BianchiDepartment of Computer Science, Stanford University, Stanford, CA 94305, USA.
Izzy GainsburgDepartment of Sociology, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0003-4363-0494
Dan JurafskyDepartment of Computer Science, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-6459-7745
Robb WillerDepartment of Sociology, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0003-3404-6472

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As generative AI enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (

Indexed as

artificial intelligencelarge language modelslaw and technologypersuasionpublic policy

Identifiers

PMID41675465
PMCPMC12887897

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.