World leaders' usage of twitter in response to the COVID-19 pandemic: A content analysis
Open accessAbstract
BACKGROUND: It is crucial that world leaders mount effective public health measures in response to COVID-19. Twitter may represent a powerful tool to help achieve this. Here, we explore the role of Twitter as used by Group of Seven (G7) world leaders in response to COVID-19. METHODS: This was a qualitative study with content analysis. Inclusion criteria were as follows: viral tweets from G7 world leaders, attracting a minimum of 500 'likes'; keywords 'COVID-19' or 'coronavirus'; search dates 17 November 2019 to 17 March 2020. We performed content analysis to categorize tweets into appropriate themes and analyzed associated Twitter data. RESULTS: Eight out of nine (88.9%) G7 world leaders had verified and active Twitter accounts, with a total following of 85.7 million users. Out of a total 203 viral tweets, 166 (82.8%) were classified as 'Informative', of which 48 (28.6%) had weblinks to government-based sources, while 19 (9.4%) were 'Morale-boosting' and 14 (6.9%) were 'Political'. Numbers of followers and viral tweets were not strictly related. CONCLUSIONS: Twitter may represent a powerful tool for world leaders to rapidly communicate public health information with citizens. We would urge general caution when using Twitter for health information, with a preference for tweets containing official government-based information sources.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246 · see citing works
Standards for Reporting Qualitative Research: A Synthesis of Recommendations
O’Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014) · Academic Medicine · doi:10.1097/acm.0000000000000388 · Cited by 12,211 · see citing works
Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2004973 · Cited by 9,951 · see citing works
The Nature and Origins of Mass Opinion
Zaller, J. R. (1992) · doi:10.1017/cbo9780511818691 · Cited by 7,785 · cited 1× · see citing works
“11 Furthermore, the nature of communication by political leaders affects public policy outcomes. 12 Thus, political leaders can use their influence over public opinion to address public health issues, ideally by disseminating evidence-based public health information. 13 A MEDLINE search for the keywords 'COVID-19' and…”
BackgroundThe Nature and Origins of Mass Opinion.
Kenski, H. C., & Zaller, J. R. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2074664 · Cited by 5,212 · see citing works
Presumed Asymptomatic Carrier Transmission of COVID-19
Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D., Chen, L., & Wang, M. (2020) · JAMA · doi:10.1001/jama.2020.2565 · Cited by 4,496 · see citing works
Responding to Covid-19 — A Once-in-a-Century Pandemic?
Gates, B. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2003762 · Cited by 1,076 · see citing works
Hitler's Army: Soldiers, Nazis, and War in the Third Reich.
Stein, G. H., & Bartov, O. (1992) · The American Historical Review · doi:10.2307/2165605 · Cited by 276 · see citing works
Communication at the Core of Effective Public Health
Bernhardt, J. M. (2004) · American Journal of Public Health · doi:10.2105/ajph.94.12.2051 · Cited by 254 · see citing works
Social media and outbreaks of emerging infectious diseases: a systematic review of literature.
Tang, L., Bie, B., Park, S.-E., & Zhi, D. (2018) · American Journal of Infection Control · doi:10.1016/j.ajic.2018.02.010 · Cited by 230 · see citing works
Estimating the reproductive number and the outbreak size of COVID-19 in Korea
Choi, S., & Ki, M. (2020) · Epidemiology and Health · doi:10.4178/epih.e2020011 · Cited by 218 · see citing works
Hitler's army: soldiers, Nazis, and war in the Third Reich
(1992) · Choice Reviews Online · doi:10.5860/choice.29-2869 · Cited by 179 · see citing works
Shin, S.-Y., Seo, D., An, J., Kwak, H., Kim, S., Gwack, J., & Jo, M. (2016) · Scientific Reports · doi:10.1038/srep32920 · Cited by 124 · see citing works
Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data
Ahmed, W., Bath, P. A., Sbaffi, L., & Demartini, G. (2019) · Health Information & Libraries Journal · doi:10.1111/hir.12247 · Cited by 99 · see citing works
How did Ebola information spread on twitter: broadcasting or viral spreading?
Liang, H., Fung, I. C., Tse, Z. T. H., Yin, J., Chan, C., Pechta, L. E., Smith, B. J., Marquez-Lameda, R., Meltzer, M. I., Lubell, K. M., et al. (2019) · BMC Public Health · doi:10.1186/s12889-019-6747-8 · Cited by 97 · see citing works
ProMED-mail: 22 years of digital surveillance of emerging infectious diseases
Carrion, M. N., & Madoff, L. C. (2017) · International Health · doi:10.1093/inthealth/ihx014 · Cited by 91 · see citing works
Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic
Masri, S., Jia, J., Li, C., Zhou, G., Lee, M., Yan, G., & Wu, J. (2019) · BMC Public Health · doi:10.1186/s12889-019-7103-8 · Cited by 80 · see citing works
Political Will: A Bridge Between Public Health Knowledge and Action
Lezine, D. A., & Reed, G. A. (2007) · American Journal of Public Health · doi:10.2105/ajph.2007.113282 · Cited by 54 · see citing works
President Trump and health care: a content analysis of misleading statements
Hatcher, W. (2019) · Journal of Public Health · doi:10.1093/pubmed/fdz176 · Cited by 25 · see citing works
Florence Nightingale (1820–1910): Feminism and Hospital Reform
Garofalo, M. E., & Fee, E. (2010) · American Journal of Public Health · doi:10.2105/ajph.2009.188722 · Cited by 15 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [8]8. Twitter Quarterly Results: 2019 Fourth Quarter. https://investor.twitterinc.com/financial-information/quarterly-results/default.aspx (18 March, date last accessed).
- [20]20. Garofalo ME, Fee E. Feminism and hospital reform. Am J Public Health 2010;100(9):1588–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28]28. Twitter Information Operations Directed at Hong Kong. 2019. https://blog.twitter.com/en_us/topics/company/2019/information_operations_directed_at_Hong_Kong.html (25 March 2020, date last accessed). Articles from Journal of Public Health (Oxford, England) are provided here courtesy of Oxford University Press
Cited by
Sorted by citations
Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2020.1780846 · Cited by 1,162 · see citing works
What social media told us in the time of COVID-19: A scoping review
Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., & Butt, Z. A. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30315-0 · Cited by 816 · see citing works
Leveraging media and health communication strategies to overcome the COVID-19 infodemic
Mheidly, N., & Fares, J. (2020) · Journal of Public Health Policy · doi:10.1057/s41271-020-00247-w · Cited by 526 · see citing works
Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach
Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020) · Journal of Medical Internet Research · doi:10.2196/20550 · Cited by 369 · see citing works
Garcia, K., & Berton, L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107057 · Cited by 369 · see citing works
COVID-19-related misinformation on social media: a systematic review
Gabarron, E., Oyeyemi, S. O., & Wynn, R. (2021) · Bulletin of the World Health Organization · doi:10.2471/blt.20.276782 · Cited by 345 · see citing works
Topics, trends, and sentiments of tweets about the COVID-19 pandemic: Temporal infoveillance study
Chandrasekaran, R., Mehta, V., Valkunde, T., & Moustakas, E. (2020) · Journal of Medical Internet Research · doi:10.2196/22624 · Cited by 214 · see citing works
COVID-19 predictability in the United States using Google Trends time series
Mavragani, A., & Gkillas, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-77275-9 · Cited by 140 · see citing works
Lalot, F., Heering, M. S., Rullo, M., Travaglino, G. A., & Abrams, D. (2020) · Group Processes & Intergroup Relations · doi:10.1177/1368430220967986 · Cited by 113 · see citing works
The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic
Haman, M. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05540 · Cited by 111 · see citing works
Impact of COVID-19 Pandemic in Public Mental Health: An Extensive Narrative Review
Clemente‐Suárez, V. J., Navarro-Jiménez, E., Jiménez, M., Hormeño-Holgado, A. J., Martínez–González, M. B., Benítez-Agudelo, J. C., Perez-Palencia, N., Laborde-Cárdenas, C. C., & Tornero-Aguilera, J. F. (2021) · Sustainability · doi:10.3390/su13063221 · Cited by 103 · see citing works
Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication
Pascual‐Ferrá, P., Alperstein, N. M., & Barnett, D. J. (2020) · Disaster Medicine and Public Health Preparedness · doi:10.1017/dmp.2020.347 · Cited by 82 · see citing works
Kostkova, P., Saigí-Rubió, F., Eguia, H. A., Borbolla, D. A., Verschuuren, M., Hamilton, C., Muscat, N. A., & Novillo-Ortiz, D. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.707902 · Cited by 83 · see citing works
Tunca, S., Sezen, B., & Wilk, V. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00773-w · Cited by 80 · see citing works
Social media mining under the COVID-19 context: Progress, challenges, and opportunities
Huang, X., Wang, S., Zhang, M., Hu, T., Hohl, A., She, B., Gong, X., Li, J., Liu, X., Gruebner, O., et al. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102967 · Cited by 79 · see citing works
Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data
Braig, N., Benz, A., Voth, S., Breitenbach, J., & Buettner, R. (2023) · IEEE Access · doi:10.1109/access.2023.3242234 · Cited by 89 · see citing works
Vlasschaert, C., Topf, J. M., & Hiremath, S. S. (2020) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2020.08.003 · Cited by 70 · see citing works
Li, Y., Twersky, S. E., Ignace, K., Zhao, M., Purandare, R., Bennett-Jones, B., & Weaver, S. R. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186847 · Cited by 70 · see citing works
Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak
Kausar, M. A., Soosaimanickam, A., & Nasar, M. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120252 · Cited by 70 · see citing works
Thelwall, M., & Thelwall, S. (2020) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2020-0134 · Cited by 69 · see citing works
Wang, X., Zhang, M., Fan, W., & Zhao, K. (2022) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24576 · Cited by 67 · see citing works
Teufel, M., Schweda, A., Dörrie, N., Musche, V., Hetkamp, M., Weismüller, B., Lenzen, H., Stettner, M., Dinse, H., Bäuerle, A., et al. (2020) · Journal of Public Health · doi:10.1093/pubmed/fdaa060 · Cited by 67 · see citing works
Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media
Li, J., & Chang, X. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10296-z · Cited by 67 · see citing works
Revealing industry challenge and business response to Covid-19: a text mining approach
Yang, M., & Han, C. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-08-2020-0920 · Cited by 65 · see citing works
Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
Sleigh, J., Amann, J., Schneider, M., & Vayena, E. (2021) · BMC Public Health · doi:10.1186/s12889-021-10851-4 · Cited by 62 · see citing works
COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter
Karami, A., Zhu, M., Goldschmidt, B., Boyajieff, H. R., & Najafabadi, M. M. (2021) · Vaccines · doi:10.3390/vaccines9101059 · Cited by 60 · see citing works
Does Bitcoin React to Trump’s Tweets?
Huynh, T. L. D. (2021) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2021.100546 · Cited by 61 · see citing works
Emotions in leader’s crisis communication: Twitter sentiment analysis during COVID-19 outbreak
Kaur, M., Verma, R., & Otoo, F. N. K. (2021) · Journal of Human Behavior in the Social Environment · doi:10.1080/10911359.2020.1829239 · Cited by 59 · see citing works
Multiclass sentiment analysis on COVID-19-related tweets using deep learning models
Vernikou, S., Lyras, A., & Kanavos, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07650-2 · Cited by 58 · see citing works
Tweet, like, subscribe! Understanding leadership through social media use
Matthews, M. J., Matthews, S. H., Wang, D., & Kelemen, T. K. (2021) · The Leadership Quarterly · doi:10.1016/j.leaqua.2021.101580 · Cited by 57 · see citing works
Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic
Ahmed, M. S., Aurpa, T. T., & Anwar, M. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253300 · Cited by 53 · see citing works
Luo, H., Meng, X., Zhao, Y., & Cai, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107733 · Cited by 52 · see citing works
Bonifazi, G., Breve, B., Cirillo, S., Corradini, E., & Virgili, L. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103095 · Cited by 46 · see citing works
Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach
Nanath, K., & Joy, G. (2021) · Behaviour and Information Technology · doi:10.1080/0144929x.2021.1941259 · Cited by 45 · see citing works
A Content and Sentiment Analysis of Greek Tweets during the Pandemic
Kydros, D. A., Argyropoulou, M., & Vrana, V. G. (2021) · Sustainability · doi:10.3390/su13116150 · Cited by 44 · see citing works
Lyu, J. C., & Luli, G. K. (2021) · Journal of Medical Internet Research · doi:10.2196/25108 · Cited by 43 · see citing works
Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis
Koren, A., Alam, M. A. U., Koneru, S., DeVito, A., Abdallah, L., & Liu, B. (2021) · JMIR Formative Research · doi:10.2196/31358 · Cited by 41 · see citing works
Using Narrative Evidence to Convey Health Information on Social Media: The Case of COVID-19
Gesser‐Edelsburg, A. (2021) · Journal of Medical Internet Research · doi:10.2196/24948 · Cited by 42 · see citing works
Andreadis, S. T., Antzoulatos, G. S., Mavropoulos, T., Giannakeris, P., Tzionis, G., Pantelidis, N., Ioannidis, K., Καρακώστας, Α., Gialampoukidis, I., Vrochidis, S., et al. (2021) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100134 · Cited by 40 · see citing works
Li, L., Aldosery, A., Vitiugin, F., Nathan, N. L., Novillo-Ortiz, D., Castillo, C., & Kostkova, P. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.716333 · Cited by 40 · see citing works
Fuentes, A., & Peterson, J. V. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251179 · Cited by 36 · see citing works
Criss, S., Nguyen, T. T. X., Norton, S., Virani, I., Titherington, E., Tillmanns, E. L., Kinnane, C., Maiolo, G., Kirby, A. B., & Gee, G. C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18115693 · Cited by 34 · see citing works
Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter
Zhang, M., Qi, X., Chen, Z., & Liu, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031651 · Cited by 34 · see citing works
Kwon, S., & Park, A. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107087 · Cited by 33 · see citing works
Saving Lives and Changing Minds with Twitter in Disasters and Pandemics: A Literature Review
Seddighi, H., Salmani, I., & Seddighi, S. (2020) · Journalism and Media · doi:10.3390/journalmedia1010005 · Cited by 33 · see citing works
HunEmBERT: A Fine-Tuned BERT-Model for Classifying Sentiment and Emotion in Political Communication
Üveges, I., & Ring, O. (2023) · IEEE Access · doi:10.1109/access.2023.3285536 · Cited by 33 · see citing works
Politicians on Social Media. The online database of members of national parliaments on Twitter
Haman, M., & Školník, M. (2021) · El Profesional de la Informacion · doi:10.3145/epi.2021.mar.17 · Cited by 28 · see citing works
Papageorgiou, M. M., & de Melo, D. D. S. N. (2022) · Fudan Journal of the Humanities and Social Sciences · doi:10.1007/s40647-022-00344-y · Cited by 27 · see citing works
Gesser‐Edelsburg, A., & Hijazi, R. (2020) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s280952 · Cited by 26 · see citing works
Senapati, A., Khan, N. A., & Chebrolu, L. B. (2020) · Methodist DeBakey Cardiovascular Journal · doi:10.14797/mdcj-16-3-e1 · Cited by 25 · see citing works
Schück, S., Foulquié, P., Mebarki, A., Faviez, C., Khadhar, M., Texier, N., Katsahian, S., Burgun, A., & Chen, X. (2021) · JMIR Formative Research · doi:10.2196/23593 · Cited by 21 · see citing works
Mittal, R., Ahmed, W., Mittal, A., & Aggarwal, I. (2021) · Information Discovery and Delivery · doi:10.1108/idd-08-2020-0102 · Cited by 24 · see citing works
Chronic Pain Management during the Covid-19 Pandemic: A Scoping Review
de Moraes, É. B., Garcia, J. B. S., Antunes, J. D. M., Daher, D. V., Seixas, F. L., & Ferrari, M. F. M. (2020) · Pain Management Nursing · doi:10.1016/j.pmn.2020.11.010 · Cited by 25 · see citing works
COVID-19 forecasts using Internet search information in the United States
Ma, S., & Yang, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-15478-y · Cited by 24 · see citing works
Chum, A., Nielsen, A., Bellows, Z., Farrell, E., Durette, P.-N., Banda, J. M., & Cupchik, G. C. (2021) · Journal of Medical Internet Research · doi:10.2196/28716 · Cited by 22 · see citing works
Chen, Y., Niu, H., & Silva, E. A. (2022) · Cities · doi:10.1016/j.cities.2022.104054 · Cited by 22 · see citing works
VADER vs. BERT: A Comparative Performance Analysis for Sentiment on Coronavirus Outbreak
Saha, S., Showrov, M. I. H., Rahman, M. M., & Majumder, M. Z. H. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34619-4_30 · Cited by 22 · see citing works
Baboukardos, D., Gaia, S., & She, C. (2021) · Corporate Governance · doi:10.1108/cg-09-2020-0438 · Cited by 22 · see citing works
Benis, A., Chatsubi, A., Levner, E., & Ashkenazi, S. (2021) · JMIR Infodemiology · doi:10.2196/31983 · Cited by 21 · see citing works
Goel, R., & Sharma, R. R. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00756-w · Cited by 20 · see citing works
Shankar, S., & Tewari, V. (2021) · Journal of Intelligence · doi:10.3390/jintelligence9040056 · Cited by 20 · see citing works
Impact of correcting misinformation on social disruption
Iizuka, R., Toriumi, F., Nishiguchi, M., Takano, M., & Yoshida, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265734 · Cited by 29 · see citing works
Lanuza, A. T., Altuzarra, A., Moreno‐Jiménez, J. M., & Navarro, J. (2022) · Public Health · doi:10.1016/j.puhe.2022.12.003 · Cited by 20 · see citing works
Lin, B., Zou, L., Duffield, N. G., Mostafavi, A., Cai, H., Zhou, B., Tao, J., Yang, M., Mandal, D., & Abedin, J. (2022) · International Journal of Digital Earth · doi:10.1080/17538947.2022.2070677 · Cited by 20 · see citing works
Amaral, A. D. R., Jung, A.-K., Braun, L.-M., & Blanco, B. (2022) · Media and Communication · doi:10.17645/mac.v10i2.5037 · Cited by 21 · see citing works
Wang, H., & Xiong, L. (2022) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2022.102925 · Cited by 19 · see citing works
Twitter Mediated Sociopolitical Communication During the COVID-19 Pandemic Crisis in India
Jain, N., Malviya, P., Singh, P., & Mukherjee, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.784907 · Cited by 18 · see citing works
Taraktaş, B., Esen, B., & Üsküdarlı, S. (2022) · Government and Opposition · doi:10.1017/gov.2022.34 · Cited by 18 · see citing works
The Role of Twitter in the WHO’s Fight against the Infodemic
Sastre, D. M., Martín, L. R., & Martín, I. R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182211990 · Cited by 18 · see citing works
Xu, Y., Yu, J., & Löffelholz, M. (2022) · Journalism Practice · doi:10.1080/17512786.2022.2058063 · Cited by 17 · see citing works
Sentiment Analysis of Covid19 Vaccines Tweets Using NLP and Machine Learning Classifiers
Rawat, A. J., Maheshwari, H., Khanduja, M., Kumar, R., Memoria, M., & Kumar, S. (2022) · International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · doi:10.1109/com-it-con54601.2022.9850629 · Cited by 17 · see citing works
A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media
Butt, M. J., Malik, A. K., Qamar, N., Yar, S., Malik, A. J., & Rauf, U. (2023) · Sensors · doi:10.3390/s23125543 · Cited by 17 · see citing works
The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review
Shoaei, M. D., & Dastani, M. (2020) · Acta Informatica Pragensia · doi:10.18267/j.aip.138 · Cited by 17 · see citing works
Tweeting During the Covid-19 Pandemic: Sentiment Analysis of Twitter Messages by President Trump
Yaqub, U. (2021) · Digital Government Research and Practice · doi:10.1145/3428090 · Cited by 16 · see citing works
New Normal Policy: Promosi Kebijakan Pariwisata Dalam Rangka Percepatan Penanganan Dampak Covid-19
Mutiarin, D., Utami, S. S., & Damanik, J. (2021) · Jurnal Kepariwisataan Destinasi Hospitalitas dan Perjalanan · doi:10.34013/jk.v5i1.277 · Cited by 16 · see citing works
Ng, Q. X., Lee, D. Y. X., Ng, C. X., Yau, C. E., Lim, Y. L., & Liew, T. M. (2023) · Vaccines · doi:10.3390/vaccines11061018 · Cited by 16 · see citing works
Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic
Soomro, Z. T., Ilyas, S. H. W., & Yaqub, U. (2020) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc51023.2020.9348291 · Cited by 16 · see citing works
Zou, C., Wang, X., Xie, Z., & Li, D. (2020) · medRxiv · doi:10.1101/2020.07.25.20162024 · Cited by 16 · see citing works
Singhal, A., Baxi, M. K., & Mago, V. (2022) · JMIR Medical Informatics · doi:10.2196/37829 · Cited by 15 · see citing works
National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis
Wang, Y., Croucher, S. M., & Pearson, E. (2021) · Frontiers in Communication · doi:10.3389/fcomm.2021.732399 · Cited by 15 · see citing works
Political Leaders Communication: A Twitter Sentiment Analysis during Covid-19 Pandemic
Kaur, M., Verma, R., & Ranjan, S. (2021) · Jurnal The Messenger · doi:10.26623/themessenger.v13i1.2585 · Cited by 15 · see citing works
Cambronero-Saiz, B., & Nieto, B. G. (2021) · Doxa Comunicación Revista interdisciplinar de estudios de comunicación y ciencias sociales · doi:10.31921/doxacom.n33a868 · Cited by 15 · see citing works
Abbas, H., Tahoun, M. M., Aboushady, A. T., Khalifa, A., Corpuz, A., & Nabeth, P. (2022) · BMJ Global Health · doi:10.1136/bmjgh-2022-008759 · Cited by 15 · see citing works
Izquierdo‐Condoy, J. S., Arias-Intriago, M., Mosquera-Quiñónez, M., Munoz, F., Jiménez-Ascanio, M., Loaiza-Guevara, V., & Ortiz‐Prado, E. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2474129 · Cited by 14 · see citing works
Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic
Vaiyapuri, T., Jagannathan, S. K., Ahmed, M. A., Ramya, K. C., Joshi, G. P., Lee, S., & Lee, G. (2023) · Sustainability · doi:10.3390/su15086404 · Cited by 14 · see citing works
Ahmed, W., Önkal, D., Das, R., Krishnan, S., Olan, F., Hardey, M., & Fenton, A. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3273191 · Cited by 16 · see citing works
Lordan, R., & Devkota, H. P. (2023) · Exploration of Digital Health Technologies · doi:10.37349/edht.2023.00002 · Cited by 14 · see citing works
Pulido, C., Ovseiko, P. V., Palomar, M. F., Kumpulainen, K., & Ramis‐Salas, M. (2021) · International Journal of Qualitative Methods · doi:10.1177/16094069211050163 · Cited by 14 · see citing works
Kalantari, N., Liao, D., & Motti, V. G. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671740 · Cited by 14 · see citing works
Aldekhyyel, R. N., Binkheder, S., Aldekhyyel, S. N., Alhumaid, N., Hassounah, M. M., AlMogbel, A., & Jamal, A. A. (2022) · Public Health in Practice · doi:10.1016/j.puhip.2022.100257 · Cited by 14 · see citing works
Zahry, N. R., McCluskey, M., & Ling, J. (2022) · Journal of Contingencies and Crisis Management · doi:10.1111/1468-5973.12412 · Cited by 14 · see citing works
Visualizing Social Media Research in the Age of COVID-19
Michailidis, P. D. (2022) · Information · doi:10.3390/info13080372 · Cited by 14 · see citing works
Liu, A. K. C., Ophir, Y., Tsai, S.-A., Walter, D., & Himelboim, I. (2022) · New Media & Society · doi:10.1177/14614448221099173 · Cited by 13 · see citing works
Sued, G., & Cebral-Loureda, M. (2020) · Revista de Comunicación y Salud · doi:10.35669/rcys.2020.10(2).549-568 · Cited by 13 · see citing works
Cebral-Loureda, M., & Sued, G. (2021) · Cuadernos info · doi:10.7764/cdi.49.27467 · Cited by 13 · see citing works
Mishra, A. K. (2020) · Wiley eBooks · doi:10.1002/9781119602927.ch2 · Cited by 12 · see citing works
Social Connectivity, Sentiment and Participation on Twitter during COVID-19
Castro-Martínez, A., Méndez-Domínguez, P., Valcárcel, A. S., & de Mesa, J. C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168390 · Cited by 13 · see citing works
Twitter Sentiment Analysis Using K-means and Hierarchical Clustering on COVID Pandemic
Kaushik, N., & Bhatia, M. K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2594-7_61 · Cited by 12 · see citing works
The role of science in a crisis: Talks by political leaders during the COVID-19 pandemic
Loner, E., Fattorini, E., & Bucchi, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282529 · Cited by 11 · see citing works
- Authors
- Rufai, S. R., & Bunce, C.
- Affiliations
- University of Leicester (GB)
- King's College London (GB)
- Corresponding author
- Rufai, Sohaib R
- Venue
- Journal of Public Health
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-20
- Volume
- 42
- Issue
- 3
- Pages
- 510-516
- ISSN
- 0943-1853 (ISSN-L)
- 1741-3842
- 1741-3850
- PMC
- PMC7188178
- OpenAlex
- W3016994297
- Cited by
- 308
- References
- 20
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 30 · 2023: 52 · 2022: 76 · 2021: 96 · 2020: 31
- FWCI
- 76.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of Public Health (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health Research
- Guy's and St Thomas' NHS Foundation Trust
- King’s College London
- Department of Health and Social Care
- Topics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Health › Social Media in Health Education
- Social Sciences › Communication › Public Relations and Crisis Communication
- Keywords
- Twitter, COVID-19 pandemic, social media communication, public health communication, political communication
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Communication, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Public Health, Global Health, Practice Guidelines as Topic, Coronavirus Infections, Information Dissemination, Qualitative Research, Pandemics, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
