Evidence map›Paper›PMID 41884546›Full record

ArticleFrontiers in psychology2026

The paradox of AI content labeling: how clarity influences information avoidance via cognitive dissonance on social platforms.

Zhixuan Gong, Danling Peng, Jinwei Cui, Zhuoru Lv

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Zhixuan GongSchool of Design, Hunan University, Changsha, Hunan, China.
Danling PengSchool of Architecture and Art, Central South University, Changsha, Hunan, China.
Jinwei CuiCollege of Literature, Hunan Normal University, Changsha, Hunan, China.
Zhuoru LvMedia and Arts Laboratory, School of Journalism and Cultural Communication, Zhongnan University of Economics and Law, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid growth of AI-generated content (AIGC) on social media has led to the introduction of AI disclosure labels to enhance transparency; however, emerging technologies such as Sora2 make it difficult for users to discern synthetic from human-created content, presenting challenges for both users and platform designers. Methods: This study investigates how different AI labels (clear, ambiguous, and no label) affect user behavior, focusing on information avoidance. We performed two online experiments ( Results: We found that ambiguous AI labels functioned as heuristic barriers that significantly increased information avoidance compared to clear or no labels. Cognitive dissonance was identified as a key mediator, where conflicting information led to discomfort and subsequent disengagement. Furthermore, factors such as label-content congruence and thematic relevance moderated these impacts. Discussion: These findings suggest that while AI disclosure labels are intended to improve transparency, ambiguous labels may inadvertently hinder user engagement, offering important implications for the design of transparency tools in AI-driven social media environments.

Indexed as

AI-generated contentAI labelsAI-mediated communicationcognitive dissonanceinformation avoidancesocial media transparency

Identifiers

PMID41884546
PMCPMC13008947

What OpenQuestion holds

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Registered trials

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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.