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The Presence of Unexpected Biases in Online Fact-checking

DC Field Value Language
dc.contributor.authorSungkyu Park-
dc.contributor.authorJeong-han Kang-
dc.contributor.authorJaimie Yejean Park-
dc.contributor.authorMeeyoung Cha-
dc.date.accessioned2024-06-20T07:50:13Z-
dc.date.available2024-06-20T07:50:13Z-
dc.date.created2022-03-25-
dc.date.issued2021-01-
dc.identifier.issn2766-1652-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/15297-
dc.description.abstractThe increasing amount of information online makes it challenging to judge what to believe or discredit. Fact-checking unverified claims shared on platforms, like social media, can play a critical role in correcting misbeliefs. The current study demonstrates how the effect of fact-checking can vary by several factors. We show that fact-checking helps self-correct one's views among young adults. However, this effect is weaker for individuals who perceived the claim negatively at first. Furthermore, borderline messages like “Lack of Evidence” can be perceived as false rather than neutral. We explain these biases via human cognitive mechanisms that avoid risk and uncertainty.-
dc.language영어-
dc.publisherHarvard Kennedy School-
dc.titleThe Presence of Unexpected Biases in Online Fact-checking-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.scopusid2-s2.0-85107974086-
dc.identifier.rimsid77936-
dc.contributor.affiliatedAuthorSungkyu Park-
dc.contributor.affiliatedAuthorMeeyoung Cha-
dc.identifier.doi10.37016/mr-2020-53-
dc.identifier.bibliographicCitationHarvard Kennedy School Misinformation Review, v.2, no.1, pp.1 - 11-
dc.relation.isPartOfHarvard Kennedy School Misinformation Review-
dc.citation.titleHarvard Kennedy School Misinformation Review-
dc.citation.volume2-
dc.citation.number1-
dc.citation.startPage1-
dc.citation.endPage11-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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