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Exposure to one-sided messaging and belief in voter fraud: the curious case of the US 2020 presidential election

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dc.contributor.authorGomez, Daniel M.-
dc.contributor.authorJenkins, Matthew David-
dc.date.accessioned2025-09-02T05:30:12Z-
dc.date.available2025-09-02T05:30:12Z-
dc.date.issued2025-07-
dc.identifier.issn1745-7289-
dc.identifier.issn1745-7297-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/79692-
dc.description.abstractIn lead-up to the 2020 U.S. presidential election, conservative media amplified Donald Trump’s concerns about the security of US elections by publishing uncritical one-sided coverage that supported the idea that US elections were particularly vulnerable to fraud. The vast majority of existing scholarship would lead us to expect that exposure to such one-sided news stories should increase belief in the vulnerability of US elections. Drawing on a survey experiment conducted shortly before the 2020 election, we show that exposure to one-sided news coverage did not have such an effect. We explain this as a result of treatment heterogeneity among partisan groups, and we use regression and random forest estimation techniques to identify major sources of treatment effect heterogeneity. We conclude with a discussion of the implications for the belief in unsubstantiated falsehoods.-
dc.format.extent22-
dc.language영어-
dc.language.isoENG-
dc.publisherRoutledge-
dc.titleExposure to one-sided messaging and belief in voter fraud: the curious case of the US 2020 presidential election-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1080/17457289.2025.2514203-
dc.identifier.scopusid2-s2.0-105013773113-
dc.identifier.wosid001553518300001-
dc.identifier.bibliographicCitationJournal of Elections, Public Opinion and Parties, v.35, no.3, pp 495 - 516-
dc.citation.titleJournal of Elections, Public Opinion and Parties-
dc.citation.volume35-
dc.citation.number3-
dc.citation.startPage495-
dc.citation.endPage516-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaGovernment & Law-
dc.relation.journalWebOfScienceCategoryPolitical Science-
dc.subject.keywordPlusNEWS-
dc.subject.keywordPlusPARTISAN-
dc.subject.keywordPlusMODEL-
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