Improving news veracity discernment with inductive learning and gamification: New insights from receiver operating characteristic analysis

Abstract

Failure to tell apart true and false news can have devastating consequences. Therefore, in this thesis, I present three papers completed in collaboration with my supervisors that, taken together, investigated how people discriminate between true and false news, and what can be done to improve this discrimination. In Paper 1, I scrutinised the effectiveness of two popular misinformation interventions: Bad News and Go Viral!. Specifically, I used receiver operating characteristic analysis to reanalyse data from five papers (k = 13; n = 17,867). In contrast to what was reported in these papers, Bad News and Go Viral! did not improve true and false news discrimination, but rather elicited conservative responding (i.e., a tendency to rate all news as false). In Paper 2, I examined what decision strategies people use to discriminate between true and false news. Accordingly, in a preregistered study (N = 327), participants rated the veracity of news headlines and indicated what decision strategy they used to make each rating. Participants discriminated between true and false news well despite choosing guess and intuition 63% of the time and only choosing rule and prior knowledge 21% of the time. Since true and false news discrimination may predominantly be a tacit (rather than explicit) process, I reasoned that providing explicit guidance to improve it (a key feature of Bad News and Go Viral!) might have limited success. Therefore, in Paper 3, I created an inductive learning intervention that involves observing true and false news headlines and classifying them as either true or false with immediate feedback (but no explicit guidance). Overall, across three preregistered experiments (N = 1,135), the intervention improved true and false news discrimination.

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Identifiers

ORCID for Ariana Modirrousta-Galian: orcid.org/0000-0003-2925-2976

ORCID for Philip Higham: orcid.org/0000-0001-6087-7224

ORCID for Tina Seabrooke: orcid.org/0000-0002-4119-8389

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