Evidence map›Paper›PMID 41394042›Full record

ArticleFrontiers in psychology2025

Analyzing the persuasion mechanism of AI-generated rumors via the elaboration likelihood model.

Zhengdong Hou

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Zhengdong HouShantou University, Guangdong, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While the technological advancements of Generative Artificial Intelligence are widely recognized, how they reshape the psychological mechanisms of human persuasion and information processing remains underexplored. This study addresses this gap by examining the persuasion mechanisms of AI-generated rumors on internet users, drawing on the Elaboration Likelihood Model (ELM). Methods: A systematic content analysis was conducted on a large dataset of 11,942 online comments responding to various AI-generated rumors. Using an established coding scheme and a reliability testing procedure, each comment was classified as indicative of either central or peripheral route processing. Results: The analysis reveals that 90.5% of the comments demonstrated peripheral route processing, with emotional expression as the primary indicator. Only 9.5% of the comments reflected central route processing, most of which involved users providing reasons or evidence, or questioning the source. Discussion: We argue that the "technological realism" of AI-generated content plays a key role in this pattern. It diminishes users' ability and motivation to engage in deeper cognitive elaboration, leading them to rely predominantly on the peripheral route for persuasion. These findings extend the Elaboration Likelihood Model to the age of AI and offer practical insights for online platform management, cybersecurity enhancement, and public education in digital literacy.

Indexed as

digital literacydisinformationelaboration likelihood modelgenerative AIonline rumors

Identifiers

PMID41394042
PMCPMC12698521

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.