An AI-Powered Lateral-Reading Tool for Assessing Online Information Credibility

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.