Curtailing False News, Amplifying Truth
SSRN Electronic Journal
2023
- 2Citations
- 3,521Usage
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We develop a comprehensive framework to assess policy measures aimed at curbing false news dissemination on social media. A randomized experiment on Twitter during the 2022 U.S. mid-term elections evaluates such policies as priming the awareness of misinformation, fact-checking, confirmation clicks, and prompting careful consideration of content. Priming is the most effective policy in reducing sharing of false news while increasing sharing of true content. A model of sharing decisions, motivated by persuasion, partisan signaling, and reputation concerns, predicts that policies affect sharing through three channels: (i) changing perceived veracity and partisanship of content, (ii) raising the salience of reputation, and (iii) increasing sharing frictions. Structural estimation shows that all policies impact sharing via the salience of reputation and cost of friction. Affecting perceived veracity plays a negligible role as a mechanism in all policies, including fact-checking. The priming intervention performs best in enhancing reputation salience with minimal added friction.
Bibliographic Details
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