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  4. Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines

Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines

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Author(s)
Ross, Robert M
•
Rand, David Gertler
•
Pennycook, Gordon
Date Issued
November 13, 2019
Publisher
Center for Open Science
Citation
Ross, Robert M, Rand, David Gertler and Pennycook, Gordon. 2019. "Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines."
Version
Final published version
Abstract

Why is misleading partisan content believed and shared? An influential account posits that political partisanship pervasively biases reasoning, such that engaging in analytic thinking exacerbates motivated reasoning and, in turn, the acceptance of hyperpartisan content. Alternatively, it may be that susceptibility to hyperpartisan content is explained by a lack of reasoning. Across two studies using different participant pools (total N = 1,973 Americans), we had participants assess true, false, and hyperpartisan news headlines taken from social media. We found no evidence that analytic thinking was associated with judging politically consistent hyperpartisan or false headlines to be accurate and unbiased. Instead, analytic thinking was associated with an increased capacity to discern between true headlines and both false and hyperpartisan headlines in most cases (and never associated with decreased discernment). These results suggest that reasoning typically helps people differentiate between low and high quality political news, rather than facilitate belief in misleading content. Because social media plays an important role in the dissemination of misinformation, we also investigated willingness to share headlines on social media. We found a similar pattern whereby analytic thinking was not associated with increased willingness to share hyperpartisan or false headlines. Together, these results suggest a positive role for reasoning in resisting misinformation.

MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 3.0 unported license
https://creativecommons.org/licenses/by/3.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/144269
DOI of Published Version
https://doi.org/10.31234/osf.io/cgsx6
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