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      A Matter of Misunderstanding? Explaining (Mis)Perceptions of Electoral Integrity across 25 Different Nations

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          Abstract

          In this paper, we investigate how trust in traditional and social media correlate with misperceptions of electoral integrity. Relying on insights from political communication research on exposure to misinformation and selective exposure mechanisms, as well as insights on the different roles of traditional and social media in different regime types, we argue that misperceptions of election integrity are likely driven in large part by the interplay between the trust people have in different media sources and the context (i.e., the level of press freedom) in which the elections take place. Using data from a survey conducted in 25 countries across the world, we find that trust in information from traditional media decreases misperceptions, while trust in information from social media increases misperceptions. However, both these effects are smaller when press freedom is restricted. In countries with low levels of press freedom, trust in social media is even associated with lower levels of misperceptions.

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          Most cited references47

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          Fake news on Twitter during the 2016 U.S. presidential election

          The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
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            Prevalence of Health Misinformation on Social Media: Systematic Review

            Background Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. Objective This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. Methods We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. Results A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%). Conclusions The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.
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              How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic in the U.S.

              We have yet to know the ultimate global impact of the novel coronavirus pandemic. However, we do know that delays, denials and misinformation about COVID-19 have exacerbated its spread and slowed pandemic response, particularly in the U.S. (e.g., Abutaleb et al., 2020).
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                Author and article information

                Contributors
                Journal
                Public Opin Q
                Public Opin Q
                poq
                Public Opinion Quarterly
                Oxford University Press
                0033-362X
                1537-5331
                2024
                16 July 2024
                16 July 2024
                : 88
                : SI , Special Issue: Public Trust in Elections. Nicholas Kerr, Bridgett A. King, and Michael Wahman, Editors
                : 495-515
                Affiliations
                Professor, Strategic Communication Group, Wageningen University & Research , Wageningen, The Netherlands
                Professor, Department of Political Science, Radboud University Nijmegen , Nijmegen, The Netherlands
                Professor, Strategic Communication Group, Wageningen University & Research , Wageningen, The Netherlands
                Associate Professor, Department of Political Science, Radboud University Nijmegen , Nijmegen, The Netherlands
                Author notes
                Corresponding author: Rens Vliegenthart, Strategic Communication Group, Wageningen University & Research, P.O. Box 8130, 6700 EW Wageningen, The Netherlands; email: rens.vliegenthart@ 123456wur.nl .
                Author information
                https://orcid.org/0000-0003-2401-2914
                https://orcid.org/0000-0002-9844-9097
                https://orcid.org/0000-0002-8053-8200
                https://orcid.org/0000-0002-4485-4927
                Article
                nfae021
                10.1093/poq/nfae021
                11300040
                37b591d2-b261-43ea-b1f0-a51056c2843a
                © The Author(s) 2024. Published by Oxford University Press on behalf of American Association for Public Opinion Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits noncommercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial reuse, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 22
                Funding
                Funded by: DATADRIVEN;
                Funded by: NORFACE;
                Funded by: Democratic Governance in a Turbulent Age;
                Funded by: Economic and Social Research Council, DOI 10.13039/501100000269;
                Funded by: Austrian Science Fund, DOI 10.13039/501100002428;
                Funded by: Dutch Research Council, DOI 10.13039/501100003246;
                Funded by: European Commission, DOI 10.13039/501100000780;
                Funded by: Horizon 2020, DOI 10.13039/100010661;
                Award ID: 822166
                Categories
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                AcademicSubjects/SOC00010

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