ORIGINAL RESEARCH article
Front. Psychol.
Sec. Cognitive Science
Analyzing the Persuasion Mechanism of AI-Generated Rumors via the Elaboration Likelihood Model
Provisionally accepted
- Shantou University, Guangdong province, 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
While the technological advancements of Generative Artificial Intelligence are widely acknowledged, the fundamental ways in which they reshape the psychological mechanisms of human persuasion and information processing remain underexplored. This study aims to fill this gap by investigating the persuasion mechanism of AI-generated rumors on internet users. Grounded in the Elaboration Likelihood Model, this study conducts a systematic content analysis of a large dataset comprising 11,942 online comments on various AI-generated rumors. Based on an established coding scheme and a reliability test procedure, comments were classified as indicative of either central or peripheral route processing. The analysis reveals that an overwhelming 90.5% of comments showed peripheral route processing, with emotional expression being the primary indicator. Only 9.5% of comments showed central route processing. These comments mainly involved users giving reasons or evidence, and questioning the source. We believe that the "technological realism" of AI-generated content plays a key role here. It weakens users' ability and desire to think deeply about information, causing them to mostly use the peripheral route for persuasion. Our findings add to the Elaboration Likelihood Model (ELM) for the age of AI. They also offer useful advice for managing online platforms, improving cyber security, and educating the public about digital literacy.
Keywords: Generative AI, disinformation, Elaboration Likelihood Model, OnlineRumors, Digital Literacy
Received: 05 Aug 2025; Accepted: 29 Oct 2025.
Copyright: © 2025 Hou. 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: Zhengdong Hou, zhendonghou@stu.edu.cn
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.