Year: 2025
Type: article
Abstract: Online misinformation distorts public opinion and is a serious societal threat. Traditional methods of detecting misinformation typically focus on a single document, restricting content consumers to a limited perspective. Lateral reading addresses this limitation by cross-verifying multiple sources to evaluate information credibility. However, manual lateral reading is challenging because it demands significant cognitive resources. Guided by persuasion and communication research, we develop an AI-powered lateral-reading tool that automatically gathers relevant information beyond a single document to help content consumers judge the credibility of online content. We further investigate the effectiveness of the source-related and content-related lateral-reading information provided by our tool and examine how document characteristics moderate the impact of different lateral-reading information. This study offers a scalable solution to improving digital literacy and combating online misinformation.
Authors: Wang, Xinran, Wang, Zisu, Chen, Adela
Institution: Colorado State University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7063384101
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