Sharing is not caring : news features predict false news detection and diffusion
Misinformation research has identified numerous news story features that predict susceptibility to false news. Four of these features seem to be consistently studied and reported as problematic: belief congruence (false news that matches one’s personal beliefs about the world), political congruence (false news that matches one’s political orientation), moral-emotional language (words that share a sense of morality while being emotionally charged), and social consensus (knowing that others also believe the false news). Reported are two different paradigms where participants were asked to read through a Facebook newsfeed and either choose which posts they would share (diffusion paradigm) or choose which posts were false news (detection paradigm). First, the studies reported below were concerned with determining the effect each of these features had on the detection and diffusion of false news, while accounting for the effects of the other features. Second, the detection paradigm was also used to determine the effect base-rates had on false news detection because according to Truth-Default Theory the number of deceptive messages people encounter in the world is directly related to how accurate they will be. All of the news features were found to have at least some effect on the diffusion and detection of false news, with belief congruence (OR [subscript diffusion] = 2.8, OR [subscript detection] = 1.4) and political congruence (OR [subscript diffusion] = 2.4, OR [subscript detection] = 1.3) having the strongest and most consistent effects. Regarding testing the effect of base-rates on detection accuracy, the more false news participants encountered, the more accurate they were, indicating the presence of a lie-bias. This contradicts the truth-bias prediction Truth-Default Theory makes, some of which is accounted for by the general suspicion people have of online news. Theoretical and practical implications are discussed from the perspective of today’s growing problem with online misinformation.