University community members’ perceptions of labels for online media

Abstract

Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics.