Deceptive Content Labeling Survey Data from Two U.S. Midwestern Universities

Ryan Suttle, Scott Hogan, Rachel Aumaugher, Matthew Spradling, Zak Merrigan and Jeremy Straub
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Ryan Suttle: Department of Mathematics & Applied Sciences, University of Michigan-Flint, Flint, MI 48502, USA
Scott Hogan: Department of Mathematics & Applied Sciences, University of Michigan-Flint, Flint, MI 48502, USA
Rachel Aumaugher: Department of Mathematics & Applied Sciences, University of Michigan-Flint, Flint, MI 48502, USA
Matthew Spradling: Department of Mathematics & Applied Sciences, University of Michigan-Flint, Flint, MI 48502, USA
Zak Merrigan: Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA
Jeremy Straub: Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA

Data, 2022, vol. 7, issue 3, 1-10

Abstract: Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating to how news consumption decisions are made and the impact of labels on decision making. This article describes these three surveys and the data that were collected by them.

Keywords: fake news; media labeling; misinformation; perceptions of labeling online content; deliberately deceptive content (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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