ORIGINAL RESEARCH article
Front. Psychol.
Sec. Media Psychology
The Paradox of AI Content Labeling: How Clarity Influences Information Avoidance via Cognitive Dissonance on Social Platforms
Zhixuan Gong 1
Jinwei Cui 2
Danling Peng 3
Zhuoru Lv 4
1. Hunan University School of Design, Changsha, China
2. Central South University School of Architecture and Art, Changsha, China
3. College of Literature, Hunan Normal University, Changsha, China
4. School of Journalism and Cultural Communication, Zhongnan University of Economics and Law, Wuhan, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract
The rapid growth of AI-generated content (AIGC) on social media platforms has led to the introduction of AI disclosure labels to enhance transparency and manage misinformation. However, the rise of advanced generative AI technologies such as Sora2 has made it increasingly difficult for users to discern AI-generated content from human-created content. This ambiguity presents challenges for both users and platform designers. This study investigates how different types of AI disclosure labels, including clear, ambiguous, and no label, affect user behavior, with a focus on information avoidance as a key outcome. Through two experiments involving 760 participants, 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 from the content. Furthermore, contextual factors such as label-content congruence and thematic relevance moderated the impact of labels on dissonance and avoidance. These findings suggest that while AI disclosure labels are intended to improve transparency, ambiguous or unclear labels may inadvertently hinder user engagement, with important implications for the design of transparency tools in AI-driven social media environments.
Summary
Keywords
AI labels2, AI-generated content1, AI-mediated communication6, cognitive dissonance3, information avoidance4, social media transparency5
Received
21 November 2025
Accepted
11 February 2026
Copyright
© 2026 Gong, Cui, Peng and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jinwei Cui
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.