The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Reasoned Approach to Dealing With Fake News
Abstract
We now have almost no filters on information that we can access, and this requires a much more vigilant, knowledgeable reader. Learning false information from the web can have dire consequences for personal, social, and personal decision making. Given how our memory works and our biases in selecting and interpreting information, now more than ever we must control our own cognitive and affective processing. As examples: Simply repeating information can increase confidence in its perceived truth; initial incorrect information remains available and can continue to have an effect despite learning the corrected information; and we are more likely to accept information that is consistent with our beliefs. Information evaluation requires readers (a) to set and monitor their goals of accuracy, coherence, and completeness; (b) to employ strategies to achieve these goals; and (c) to value this time- and effort-consuming systematic evaluation. Several recommendations support a reasoned approach to fake news and manipulation.
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In the citing paper
“In online settings that favour speed and minimal effort, users often rely on System 1 thinking, making quick credibility judgments that are prone to cognitive biases such as confirmation bias, the tendency to favour information that supports existing beliefs while ignoring contradictions [2,12].”
From When the source is a bot: How people adapt their evaluation strategies to assess AI-generated content (2026) · cites this paper as [2]
In the citing paper
“Echo chambers that limit exposure to conflicting beliefs [2] may curate their own corpus of conspiratorial sources.”
From When the source is a bot: How people adapt their evaluation strategies to assess AI-generated content (2026) · cites this paper as [2]
Citations
Cites
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Flagged first, then by citations
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Cited by
Sorted by citations
The Emergence of Deepfake Technology: A Review
Westerlund, M. (2019) · Technology Innovation Management Review · doi:10.22215/timreview/1282 · Cited by 1,138
Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics
Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00994-6 · Cited by 591
How behavioural sciences can promote truth, autonomy and democratic discourse online
Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0889-7 · Cited by 223
Dabran-Zivan, S., Klein-Avraham, I., & Baram-Tsabari, A. (2026) · PLOS One · doi:10.1371/journal.pone.0345300 · Cited by 6
Metadata
- Authors
- Britt, M. A., Rouet, J.-F., Blaum, D., & Millis, K.
- Affiliations
- Northern Illinois University, DeKalb, USA
- Université of Poitiers, France
- Venue
- Policy Insights from the Behavioral and Brain Sciences
- Publisher
- SAGE Publications
- Published
- 2019-03
- Volume
- 6
- Issue
- 1
- Pages
- 94-101
- ISSN
- 2372-7322
- 2372-7330
- Cited by
- 81
- References
- 4
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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