{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:46:38Z","timestamp":1787071598671,"version":"build-2736575974"},"reference-count":22,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>\n                    A\n                    <jats:sc>BSTRACT<\/jats:sc>\n                  <\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>During COVID-19, the government and public health sectors used Twitter for disseminating information on health, awareness and preventing measures. However, resources on infodemics of the public responses and their personal expressions in the evolutionary stages of the pandemic at a global level is limited. We explored the topics discussed by the Tweeples related to COVID-19.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We retrieved English language tweets from 25 December 2019 to 26 March 2020 containing the keywords \u2018Corona\u2019 or \u2018nCoV\u2019 or \u2018Covid\u2019 or \u2018SARS\u2019 or \u2018virus\u2019 using the Twitter\u2019s application programme interface (API) in Python 3.7 with predefined codes. Trend graphs were generated using the counts and proportions of keyword tweets and the number of retweets of the keyword tweets. We performed thematic analysis with selected 1400 (100 from every Thursday) tweets for emerging topics and critically appraised the themes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>We identified that Tweeples\u2019 were discussing the coronavirus pandemic a week before official notification. \u2018Corona\u2019 (60%) and \u2018COVID\u2019 (26%) were the most popular terms. We constructed six common themes including awareness, emotions, beliefs, politics, economy and controversies. Key topics of the tweets during the early stages were the economic impact and scientific information of COVID-19, stigma and discrimination, general awareness, personal reactions, social support and conspirational theories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>During the evolutionary phase of the COVID-19 pandemic, a rapid knowledge transition was observed. Twitter as a vibrant social media can be used for early identification of disease outbreak, perception and communication of the community, thus complementing the existing disease-surveillance and management systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jfmpc.jfmpc_1556_24","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T15:03:19Z","timestamp":1758639799000},"page":"3252-3258","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Tweeples\u2019 concerns during the early phase of COVID-19 Pandemic \u2013 Lessons for the future public health communications: A Twitter-based mixed methods study"],"prefix":"10.4103","volume":"14","author":[{"given":"Harshal B.","family":"Sonekar","sequence":"first","affiliation":[{"name":"Department of Health Systems Research, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumaravel","family":"Ilangovan","sequence":"additional","affiliation":[{"name":"Department of Health Systems Research, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suganya","family":"Barani","sequence":"additional","affiliation":[{"name":"Department of Health Systems Research, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sendhilkumar","family":"Muthappan","sequence":"additional","affiliation":[{"name":"Department of Health Systems Research, ICMR-National Institute of Epidemiology, Ayapakkam, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gowtham","family":"Sockalingam","sequence":"additional","affiliation":[{"name":"Department of Health Systems Research, ICMR-National Institute 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