Understanding how young adults assess the source credibility of misinformation and AI-generated content labels

The advent of the internet brought with it unprecedented access to information and potential for many people. However, the ease and speed with which information can be spread has also facilitated the proliferation of false and misleading information that has had a widespread and injurious impact on many aspects of people's lives, including their health, access to fair elections, and personal safety. Social media platforms have been implicated as facilitators of the spread of misinformation, and the American public has been divided in the role that social media platforms should play in the moderation of problematic content. This problem is particularly concerning for young adults, who receive a proportionally high amount of their news from social media platforms. Content labels are the preeminent means to communicate to social media users whether a social media post contains misinformation, and more recently, AI-generated content. However, research has shown that content labels are less effective on young adults. Prior work has also shown that young adults value the source of the information identification of a post (usually fact checkers, AI tools, or other social media users) in their decision to value the content label's assessment. This dissertation examines how young adults (ages 18-25) assess the source credibility of content labels for misinformation and AI-generated content. I first present findings from a survey of 1,462 adults that was implemented to establish a baseline understanding of which content label sources are more effective for young adults for different types of content (Chapter 4) as well as results from analyses of that survey data that tested for differences among different demographic groups within the young adult population (Chapter 5). I then share findings from a diary study with 22 participants and follow-up group interviews with 9 of those participants on why they found some sources more credible than others for text information and AI-generated images (Chapter 6). I found that for young adults, AI Tools are most effective for labels on false text and AI-generated images, professional fact checkers are most effective for true text labels, and labels from other social media users are most effective for labels that identify an image is not AI-generated. My findings also include key distinctions between demographic groups in their source credibility assessment of content labels as well as identify demographic groups for which the labels were least impactful. Additionally, I contribute a framework of strategies commonly used by young adults to assess the source credibility of content labels for text misinformation and AI-generated images. Finally, I discuss the implications of these findings as well as directions for future research (Chapter 7).