Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment

This study found that AIGC labels minimally affect perceived accuracy, message credibility, or sharing intention but help distinguish AIGC from human-generated content. The labels do not negatively impact users' perceptions of platform content, indicating their potential for fact-checking and governance. However, AIGC labeling applications should vary by information type; they can slightly enhance sharing intention and perceived accuracy for misinformation. This highlights the need for more nuanced strategies for AIGC labels, necessitating further research.

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Publication Details

Journal
JMIR Formative Research
Published
2024-12-24
DOI
https://doi.org/10.2196/60024
Citations
15
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
16.98

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article

Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment

Ya Yang, Fan Li
15 citations
JMIR Formative Research
Misinformation and Its Impacts
16.98
article

Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment

Ya Yang, Fan Li
article en
15 citations

Abstract

This study found that AIGC labels minimally affect perceived accuracy, message credibility, or sharing intention but help distinguish AIGC from human-generated content. The labels do not negatively impact users' perceptions of platform content, indicating their potential for fact-checking and governance. However, AIGC labeling applications should vary by information type; they can slightly enhance sharing intention and perceived accuracy for misinformation. This highlights the need for more nuanced strategies for AIGC labels, necessitating further research.

JMIR Formative ResearchVol. 8
Beijing Normal University (CN)
Beijing Normal University, National Office for Philosophy and Social Sciences
Openalex Percentile: Top 1%
Misinformation and Its Impacts
16.98
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