{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:10:25Z","timestamp":1792289425864,"version":"4.4.0"},"reference-count":49,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Rumors and conspiracy theories, can contribute to vaccine hesitancy. Monitoring online data related to COVID-19 vaccine candidates can track vaccine misinformation in real-time and assist in negating its impact. This study aimed to examine COVID-19 vaccine rumors and conspiracy theories circulating on online platforms, understand their context, and then review interventions to manage this misinformation and increase vaccine acceptance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>In June 2020, a multi-disciplinary team was formed to review and collect online rumors and conspiracy theories between 31 December 2019\u201330 November 2020. Sources included Google, Google Fact Check, Facebook, YouTube, Twitter, fact-checking agency websites, and television and newspaper websites. Quantitative data were extracted, entered in an Excel spreadsheet, and analyzed descriptively using the statistical package R version 4.0.3. We conducted a content analysis of the qualitative information from news articles, online reports and blogs and compared with findings from quantitative data. Based on the fact-checking agency ratings, information was categorized as true, false, misleading, or exaggerated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 637 COVID-19 vaccine-related items: 91% were rumors and 9% were conspiracy theories from 52 countries. Of the 578 rumors, 36% were related to vaccine development, availability, and access, 20% related to morbidity and mortality, 8% to safety, efficacy, and acceptance, and the rest were other categories. Of the 637 items, 5% (30\/) were true, 83% (528\/637) were false, 10% (66\/637) were misleading, and 2% (13\/637) were exaggerated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Rumors and conspiracy theories may lead to mistrust contributing to vaccine hesitancy. Tracking COVID-19 vaccine misinformation in real-time and engaging with social media to disseminate correct information could help safeguard the public against misinformation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0251605","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T13:53:49Z","timestamp":1620827629000},"page":"e0251605","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":447,"title":["COVID-19 vaccine rumors and conspiracy theories: The need for cognitive inoculation against misinformation to improve vaccine adherence"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5690-5971","authenticated-orcid":true,"given":"Md Saiful","family":"Islam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2529-3685","authenticated-orcid":true,"given":"Abu-Hena Mostofa","family":"Kamal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alamgir","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorothy L.","family":"Southern","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sazzad Hossain","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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