This research takes on the twin challenges of understanding what determines whether people accept or reject misleading information, and what can be done to correct such misinformation – or, better yet, prevent its dissemination in the first place. The PIs integrate political science and cognitive science to understand the conditions that influence the formation and revision of false beliefs, and how to foster the spread of accurate information. The insights that the PIs generate will help to advance theories around belief updating by reconciling apparently contradictory results regarding the role of political knowledge and sophistication from political science and cognitive science. The PIs' work advances emerging theories concerning motivations for online sharing. In addition to these intellectual benefits, the work will also directly inform efforts to encourage people to be more responsible and informed citizens, especially online. Reducing belief in, and sharing of, inaccurate information benefits democratic societies by helping citizens make decisions that reflect their preferences.

The PIs identify the cognitive basis of belief in misleading information. Prior research typically emphasizes one of two factors: political knowledge or cognitive style. However, by examining these two concepts in isolation, previous scholars have been unable to disentangle the independent influence of each factor. Across a series of proposed correlation and experimental survey studies, the PIs juxtapose political knowledge and cognitive style, in order to understand whether, and in what ways, these two factors independently shape belief in and responses to inaccurate information. Based on the results of this first strand of work, the PIs develop a set of behavioral interventions to encourage people to become more responsible citizens. While previous approaches to correcting misleading information have primarily involved the provision of specific pieces of factual information, such as fact-checking messages, They instead favor two alternative sets of strategies: (1) improving the quality of content that individuals encounter online, and (2) inducing individuals to pay greater attention to accuracy when evaluating this content. The PIs evaluate the effectiveness of such approaches using survey experiments and social media field experiments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Social and Economic Sciences (SES)
Type
Standard Grant (Standard)
Application #
2047152
Program Officer
Jan Leighley
Project Start
Project End
Budget Start
2021-07-01
Budget End
2023-06-30
Support Year
Fiscal Year
2020
Total Cost
$737,381
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
DUNS #
City
Cambridge
State
MA
Country
United States
Zip Code
02139