No abstract

No abstract

No abstract

No abstract

This article focuses on message credibility and detection accuracy of fake and real news as represented on social media. We developed a deception detection paradigm for news headlines and conducted two online experiments to examine the extent to which people (1) perceive news headlines as credible, and (2) accurately distinguish fake and real news across three general topics (i.e., politics, science, and health). Both studies revealed that people often judged news headlines as fake, suggesting a deception-bias for news in social media. Across studies, we observed an average detection accuracy of approximately 51%, a level consistent with most research using this deception detection paradigm with equal lie-truth base-rates. Study 2 evaluated the effects of endorsement cues in social media (e.g., Facebook likes) on message credibility and detection accuracy. Results showed that headlines associated with a high number of Facebook likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. These studies introduce truth-default theory to the context of news credibility and advance our understanding of how biased processing of news information can impact detection accuracy with social media endorsement cues.