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Paper Type
ERF
Abstract
The proliferation of generative AI tools raises concerns about consuming AI-generated misinformation. Although previous research has focused on the features of misinformation, fewer studies pay attention to the role of AI in generating misinformation on social media platforms. To address this gap, we propose a qualitative study to understand how users identify AI-generated misinformation on social media. First, we will examine the online discourse among social media users to gain insights into the influence of fact-checking attempts made by fellow users. Next, we will interview social media users, letting them look through social media posts to understand how they assess content credibility from both senders and other users. We expect to find unique features of AI misinformation and cues from the online environment, including users’ comments and posts that impact truth and, in turn, influence the content's believability.
Paper Number
1405
Recommended Citation
Wang, Qinhui; Baham, Corey; and Delen, Dursun, "The Impact of AI-Generated Misinformation Features on Human Believability: A Qualitative Approach" (2024). AMCIS 2024 Proceedings. 26.
https://aisel.aisnet.org/amcis2024/ai_aa/ai_aa/26
The Impact of AI-Generated Misinformation Features on Human Believability: A Qualitative Approach
The proliferation of generative AI tools raises concerns about consuming AI-generated misinformation. Although previous research has focused on the features of misinformation, fewer studies pay attention to the role of AI in generating misinformation on social media platforms. To address this gap, we propose a qualitative study to understand how users identify AI-generated misinformation on social media. First, we will examine the online discourse among social media users to gain insights into the influence of fact-checking attempts made by fellow users. Next, we will interview social media users, letting them look through social media posts to understand how they assess content credibility from both senders and other users. We expect to find unique features of AI misinformation and cues from the online environment, including users’ comments and posts that impact truth and, in turn, influence the content's believability.
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