The DOI matches its Crossref record.
How to fight an infodemic
Open accessOverview
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
Verification
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Under these current plausible COVID-19 vaccine acceptance rates, possible levels of existing protective immunity-though it is unclear whether post-infection immunity confers long-term immunity 29 and the rapidly evolving nature of misinformation surrounding the pandemic 23,30 , it is unclear whether vaccination will reach the levels required for herd immunity.”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“In addition to the physical health impacts of COVID-19, there is a rapid amplification of both accurate and inaccurate information on digital platforms worldwide [3].”
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“While accessible, non-visual techniques to present a variety of data graphics have largely been investigated in research [43,92,93,130,134], few have been translated to standard web tools for use in practice.”
From The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19 (2023) · cites this paper as [130]
Experimental — accuracy not yet measuredIn the citing paper
“Public support for policies to control the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is being undercut by misinformation, leading to the World Health Organization's 'infodemic' declaration (6).”
From Misinformation in and about science (2021) · cites this paper as [6]
In the citing paper
“Infodemic can influence medical decision-making on treatment or preventive measures, (59, 60) for example some people used hydroxychloroquine as a treatment for COVID-19 based on false or unproven information, endorsed by popular and influential people (61).”
From ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023) · cites this paper as [59]
In the citing paper
“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13,14 or inflammatory 15 content, a phenomenon named infodemics [16][17][18][19] , sharing similarities with more traditional epidemics and spreading phenomena [20][21][22] .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [12]
In the citing paper
“Lacking a developed framework, tech companies have fumbled their way through the ongoing coronavirus pandemic, unable to stem the “infodemic” of misinformation”
From Stewardship of global collective behavior (2021) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Items 401–500 of 1,000
Duong, T. V., Nguyen, M. H., Lai, C.-F., Chen, S., Dadaczynski, K., Okan, O., & Lin, C.-Y. (2022) · Annals of Medicine · doi:10.1080/07853890.2022.2101688 · Cited by 36
Overcoming the challenge of COVID‐19: A grounded theory approach to rural nurses' experiences
Ohta, R., Matsuzaki, Y., & Itamochi, S. (2020) · Journal of General and Family Medicine · doi:10.1002/jgf2.410 · Cited by 35
Women’s experience of pregnancy and birth during the COVID-19 pandemic: a qualitative study
Keating, N., Dempsey, B., Corcoran, S. M., McAuliffe, F. M., Lalor, J., & Higgins, M. (2021) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-021-02862-2 · Cited by 35
Emergence of knowledge communities and information centralization during the COVID-19 pandemic
Sacco, P. L., Gallotti, R., Pilati, F., Castaldo, N., & Domenico, M. D. (2021) · Social Science & Medicine · doi:10.1016/j.socscimed.2021.114215 · Cited by 35
Stress and cardiovascular risk burden after the pandemic: current status and future prospects
Mattioli, A. V., Coppi, F., Nasi, M., & Gallina, S. (2022) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2022.2092097 · Cited by 33
Su, Y., Borah, P., & Xiao, X. (2022) · Online Information Review · doi:10.1108/oir-06-2021-0305 · Cited by 36
Jylhä, K. M., Stanley, S. K., Ojala, M., & Clarke, E. J. R. (2022) · European Psychologist · doi:10.1027/1016-9040/a000487 · Cited by 49
Rosenberg, A., Walker, J., Griffiths, S., & Jenkins, R. (2023) · Learned Publishing · doi:10.1002/leap.1524 · Cited by 33
Mir, A. A., Sevukan, R., & Gul, S. (2021) · Library Hi Tech · doi:10.1108/lht-08-2021-0261 · Cited by 37
Ioannidis, J. P. A. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-052891 · Cited by 36
Deplatforming did not decrease Parler users’ activity on fringe social media
Ribeiro, M. H., Hosseinmardi, H., West, R., & Watts, D. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad035 · Cited by 41
Stauch, L., Renninger, D., Rangnow, P., Hartmann, A., Fischer, L. M., Dadaczynski, K., & Okan, O. (2025) · Journal of Medical Internet Research · doi:10.2196/69170 · Cited by 32
Social Emotions Under the Spread of COVID-19 Using Social Media
Toriumi, F., Sakaki, T., & Yoshida, M. (2020) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.f-k45 · Cited by 34
Llerena-Izquierdo, J., & Ayala-Carabajo, R. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-68418-1_10 · Cited by 32
Levin‐Zamir, D., Sørensen, K., Su, T. T., Sentell, T. L., Rowlands, G., Messer, M., Pleasant, A., Nunes, L. S., Lev‐Ari, S., & Okan, O. (2021) · Global Health Promotion · doi:10.1177/1757975921998639 · Cited by 36
İnfodemide Dijital Ortamda Sağlık Bilgisi Edinme ve Teyit Davranışları
Çömlekçi, M. F., & BOZKANAT, E. (2021) · İstanbul Üniversitesi Sosyoloji Dergisi / İstanbul University Journal of Sociology · doi:10.26650/sj.2021.41.1.0024 · Cited by 33
Mehta, H. B., An, H., Andersen, K. M., Mansour, O., Madhira, V., Rashidi, E., Bates, B. A., Setoguchi, S., Joseph, C., Kocis, P. T., et al. (2021) · Annals of Internal Medicine · doi:10.7326/m21-0857 · Cited by 31
Mathematical modeling applied to epidemics: an overview
da Mata, A. S., & Pereira, S. M. (2021) · São Paulo Journal of Mathematical Sciences · doi:10.1007/s40863-021-00268-7 · Cited by 35
Quansah, F., Hagan, J. E., Sambah, F., Frimpong, J. B., Ankomah, F., Srem‐Sai, M., Seibu, M., Abieraba, R. S. K., & Schack, T. (2022) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe12010003 · Cited by 33
Tahamtan, I., Potnis, D. D., Mohammadi, E., Singh, V., & Miller, L. E. (2022) · Journal of Medical Internet Research · doi:10.2196/34321 · Cited by 36
Almotairi, K. H., Hussein, A. M., Abualigah, L., Abujayyab, S. K. M., Mahmoud, E. H., Ghanem, B. O., & Gandomi, A. H. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010011 · Cited by 37
Challenges in Reporting the COVID-19 Spread and its Presentation to the Society
Kubiczek, J., & Hadasik, B. (2021) · Journal of Data and Information Quality · doi:10.1145/3470851 · Cited by 31
Yang, X., Luo, C., Xu, Y., He, Y., & Zhao, R. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102225 · Cited by 32
“Coronavirus EXPLAINED”: YouTube, COVID-19, and the Socio-Technical Mediation of Expertise
Marchal, N., & Au, H. (2020) · Social Media + Society · doi:10.1177/2056305120948158 · Cited by 31
Acuto, M., Dickey, A., Butcher, S., & Washbourne, C.-L. (2020) · World Development · doi:10.1016/j.worlddev.2020.105295 · Cited by 31
Al‐Amad, S. H., & Hussein, A. (2021) · BMC Psychology · doi:10.1186/s40359-020-00509-y · Cited by 33
The presence of unexpected biases in online fact-checking
Park, S., Park, J. Y., & Kang, J. (2021) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-53 · Cited by 31
Nguyen, T. T., Nguyen, M. H., Pham, T. T. M., Le, V.-T. T., Nguyen, T. T., Công, T. L., Binh, N., Dao, H. K., Nguyen, H. C., Ha, T. H., et al. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.774328 · Cited by 32
Health-related fake news during the COVID-19 pandemic: perceived trust and information search
Zheng, L., Elhai, J. D., Miao, M., Wang, Y., Wang, Y., & Gan, Y. (2022) · Internet Research · doi:10.1108/intr-11-2020-0624 · Cited by 33
People’s understanding of the concept of misinformation
Osman, M., Adams, Z., Meder, B., Bechlivanidis, C., Verduga, O., & Strong, C. (2022) · Journal of Risk Research · doi:10.1080/13669877.2022.2049623 · Cited by 36
Communication and crisis in the public space: Dissolution and uncertainty
Palau-Sampio, D., & García, G. L. (2022) · El Profesional de la Informacion · doi:10.3145/epi.2022.may.16 · Cited by 31
Czerniak, K. W., Pillai, R., Parmar, A., Ramnath, K., Krocker, J. D., & Myneni, S. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad005 · Cited by 31
Olamijuwon, E. O., Konje, E. T., Kansiime, C., Kesby, M., Keenan, K. L., Neema, S., Asiimwe, B. B., Mshana, S. E., Mushi, M. F., Loza, O., et al. (2023) · Antimicrobial Resistance and Infection Control · doi:10.1186/s13756-022-01199-4 · Cited by 31
COVID-19-Pandemie trifft auf Psyche der Bevölkerung: Gibt es einen Tsunami psychischer Störungen?
Riedel-Heller, S., & Richter, D. (2020) · Psychiatrische Praxis · doi:10.1055/a-1290-3469 · Cited by 40
El rol de la telesalud en la lucha contra el COVID-19 y la evolución del marco normativo peruano
Curioso, W. H., & Galán-Rodas, E. (2020) · ACTA MEDICA PERUANA · doi:10.35663/amp.2020.373.1004 · Cited by 30
AMUSED: An Annotation Framework of Multimodal Social Media Data
Shahi, G. K., & Majchrzak, T. A. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-10525-8_23 · Cited by 30
Ethics, pandemic and environment; looking at the future of low middle income countries
Tanveer, F., Khalil, A. T., Ali, M., & Shinwari, Z. K. (2020) · International Journal for Equity in Health · doi:10.1186/s12939-020-01296-z · Cited by 27
Zhang, C., Xu, S., Li, Z., & Hu, S. (2021) · Journal of Medical Internet Research · doi:10.2196/26482 · Cited by 31
Alshareef, N., Yunusa, I. U., & Al‐Hanawi, M. K. (2021) · JMIR Public Health and Surveillance · doi:10.2196/28888 · Cited by 30
Winters, M., Oppenheim, B., Sengeh, P., Jalloh, M. B., Webber, N., Pratt, S., Leigh, B., Mölsted-Alvesson, H., Zeebari, Z., Sundberg, C. J., et al. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2021-006954 · Cited by 34
Li, Z., Du, X., Zhao, Y., Tu, Y., Lev, B., & Gan, L. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103077 · Cited by 30
Doing your own research and other impossible acts of epistemic superheroism
Buzzell, A., & Rini, R. A. (2022) · Philosophical Psychology · doi:10.1080/09515089.2022.2138019 · Cited by 30
Chagas, B. A., Pagano, A. S., Prates, R. O., Praes, E. C., Ferreguetti, K., Vaz, H., Reis, Z. S. N., Ribeiro, L. B., Ribeiro, A. L. P., Pedroso, T. M., et al. (2023) · JMIR Human Factors · doi:10.2196/43135 · Cited by 30
Public Health Challenges in Saudi Arabia during the COVID-19 Pandemic: A Literature Review
Sheerah, H. A., Almuzaini, Y. S., & Khan, A. A. (2023) · Healthcare · doi:10.3390/healthcare11121757 · Cited by 33
Metcalfe, B., Boshuizen, H., Bulens, J., & Koehorst, J. (2023) · F1000Research · doi:10.12688/f1000research.137262.1 · Cited by 29
Zeng, M. L., Hong, Y., Clunis, J., He, S., & Coladangelo, L. P. (2020) · Data and Information Management · doi:10.2478/dim-2020-0009 · Cited by 29
Meaningful Big Data Integration for a Global COVID-19 Strategy
Pita Costa, J., Grobelnik, M., Fuart, F., Stopar, L., Epelde, G., Fischaber, S., Poliwoda, P., Rankin, D., Wallace, J., Black, M., et al. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.3019898 · Cited by 29
Ho, K. H. M., & Smith, G. D. (2020) · Journal of Clinical Nursing · doi:10.1111/jocn.15495 · Cited by 29
Social media and the spread of COVID-19 infodemic
Adekoya, C. O., & Fasae, J. K. (2021) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-11-2020-0165 · Cited by 34
Yassin, A., Al-Omari, M., Al-Azzam, S. I., Karasneh, R. A., Abu‐Ismail, L., & Soudah, O. (2021) · International Journal of Environmental Health Research · doi:10.1080/09603123.2021.1935778 · Cited by 36
Cancer screening in the U.S. through the COVID-19 pandemic, recovery, and beyond
Croswell, J. M., Corley, D. A., Lafata, J. E., Haas, J. S., Inadomi, J. M., Kamineni, A., Ritzwoller, D. P., Vachani, A., & Zheng, Y. (2021) · Preventive Medicine · doi:10.1016/j.ypmed.2021.106595 · Cited by 30
He, C., He, L., Lu, T., & Li, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479555 · Cited by 32
Misinformation can prevent the suppression of epidemics
Sontag, A., Rogers, T., & Yates, C. A. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0668 · Cited by 29
The global spread of misinformation on spiders
Mammola, S., Malumbres‐Olarte, J., Arabesky, V., Barrales-Alcalá, D. A., Barrion-Dupo, A. L. A., Benamú, M. A., Bird, T. L., Bogomolova, M., Cardoso, P., Chatzaki, M., et al. (2022) · Current Biology · doi:10.1016/j.cub.2022.07.026 · Cited by 31
Wismans, A., van der Zwan, P., Wennberg, K. J., Franken, I. H. A., Mukerjee, J., Baptista, R., Marín, J. B., Burke, A., Dejardin, M., Janssen, F., et al. (2022) · BMC Public Health · doi:10.1186/s12889-022-13632-9 · Cited by 29
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
Glockner, M., Hou, Y., & Gurevych, I. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.397 · Cited by 52
The Costs of Polarizing a Pandemic: Antecedents, Consequences, and Lessons
Van Bavel, J. J., Pretus, C., Rathje, S., Pärnamets, P., Vlasceanu, M., & Knowles, E. D. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231190395 · Cited by 29
Yabrude, A. T. Z., de Souza, A. C. M., de Campos, C. W., Bohn, L., & Tiboni, M. (2020) · Revista Brasileira de Educação Médica · doi:10.1590/1981-5271v44.supl.1-20200381 · Cited by 28
Llamas, N. M. (2020) · Revista Internacional de Relaciones Públicas · doi:10.5783/rirp-20-2020-07-111-134 · Cited by 28
Grey, A., & Severin, A. A. (2022) · Griffith Law Review · doi:10.1080/10383441.2022.2031526 · Cited by 28
TFGIN: Tight-Fitting Graph Inference Network for Table-based Fact Verification
Wu, L., Wang, K., Nie, K., Guo, S., Gao, C., Wang, Z., & Li, S. (2025) · ACM Transactions on Information Systems · doi:10.1145/3734520 · Cited by 36
Zhang, D., Zhou, L., & Lim, J. (2020) · Information Systems Management · doi:10.1080/10580530.2020.1820635 · Cited by 30
Refuting Spurious COVID-19 Treatment Claims Reduces Demand and Misinformation Sharing
MacFarlane, D., Tay, L. Q., Hurlstone, M. J., & Ecker, U. K. H. (2020) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2020.12.005 · Cited by 60
Han, Y., Jiang, B., & Guo, R. (2021) · Journal of Medical Internet Research · doi:10.2196/23097 · Cited by 30
Impact of COVID-19 infodemic on psychological wellbeing and vaccine hesitancy
Samal, J. (2021) · Egyptian Journal of Bronchology · doi:10.1186/s43168-021-00061-2 · Cited by 27
Body Donation, Teaching, and Research in Dissection Rooms in Spain in Times of Covid‐19
Manzanares‐Céspedes, M. C., Dalmau‐Pastor, M., de Blas, C. S., & Vázquez, T. (2021) · Anatomical Sciences Education · doi:10.1002/ase.2093 · Cited by 28
The artificial intelligence (AI) role for tackling against COVID-19 pandemic
Shamman, A. H., Hadi, A. A., Ramul, A. R., Zahra, M. M. A., & Gheni, H. M. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.07.357 · Cited by 31
Fake News on COVID-19 in Indonesia
Muzykant, V. L., Muqsith, M. A., Pratomo, R. R., & Barabash, V. (2021) · Risk, systems and decisions · doi:10.1007/978-3-030-77344-1_22 · Cited by 35
Massarani, L., & Neves, L. F. F. (2022) · Ciência & Saúde Coletiva · doi:10.1590/1413-81232022273.20512021 · Cited by 28
Madziva, R., Nachipo, B., Musuka, G., Chitungo, I. J. B., Murewanhema, G., Phiri, B., & Dzinamarira, T. (2022) · Health Promotion Perspectives · doi:10.34172/hpp.2022.03 · Cited by 28
COVID-19 Vaccine Sensing: Sentiment Analysis and Subject Distillation from Twitter Data
Han, X., Liu, R., Luo, Z., & Xu, M. (2022) · Telematics and Informatics Reports · doi:10.1016/j.teler.2022.100016 · Cited by 29
Scheffer, M. C., Paiva, V. S. F., Barberia, L., & Russo, G. (2022) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2022.100394 · Cited by 28
Raza, S. H., Yousaf, M. T., Zaman, U., Khan, S. W., Core, R. S., & Malik, A. N. (2023) · Vaccine · doi:10.1016/j.vaccine.2023.01.047 · Cited by 31
van der Linden, S., Albarracín, D., Fazio, L. K., Freelon, D., Roozenbeek, J., Swire‐Thompson, B., & Bavel, J. J. V. (2023) · PsycEXTRA Dataset · doi:10.1037/e506432023-001 · Cited by 34
Chan, A. H. Y., Rutter, V., Ashiru-Oredope, D. A. I., Tuck, C. Z., & Babar, Z.-U.-D. (2020) · Journal of Pharmaceutical Policy and Practice · doi:10.1186/s40545-020-00214-6 · Cited by 29
Liang, Z., Delvecchio, E., Buratta, L., & Mazzeschi, C. (2020) · Revista de Psicología Clínica con Niños y Adolescentes · doi:10.21134/rpcna.2020.mon.2054 · Cited by 29
Casà, C., Marotta, C., Pumpo, M. D., Cozzolino, A., D'Aviero, A., Frisicale, E. M., Silenzi, A., Gabbrielli, F., Bertinato, L., & Brusaferro, S. (2021) · Annali dell Istituto Superiore di Sanità · doi:10.4415/ann_21_01_01 · Cited by 26
The Covid 19 outbreak: Maternal Mental Health and Associated Factors
Koyucu, R. G., & Karaca, P. P. (2021) · Midwifery · doi:10.1016/j.midw.2021.103013 · Cited by 27
Community Mitigation of COVID-19 and Portrayal of Testing on TikTok: Descriptive Study
Basch, C. H., Mohlman, J., Fera, J., Tang, H., Pellicane, A., & Basch, C. E. (2021) · JMIR Public Health and Surveillance · doi:10.2196/29528 · Cited by 34
Social Media Insights During the COVID-19 Pandemic: Infodemiology Study Using Big Data
Tran, H. T. T., Lu, S., Tran, H., & Nguyen, B. V. (2021) · JMIR Medical Informatics · doi:10.2196/27116 · Cited by 27
Peramo-Álvarez, F. P., Zúñiga, M. Á. L., & López‐Ruz, M. Á. (2021) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2021.04.008 · Cited by 30
Huo, S., Wang, K., Liu, Z., Yang, Y., Hee, J. Y., He, Q., Takesue, R., & Tang, K. (2021) · JMIR Public Health and Surveillance · doi:10.2196/33394 · Cited by 27
Post-traumatic Stress and Growth Among the Children and Adolescents in the Aftermath of COVID-19
Bhushan, B., Basu, S., & Ganai, U. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.791263 · Cited by 30
Herrera‐Añazco, P., Urrunaga‐Pastor, D., Benites-Zapata, V. A., Bendezú-Quispe, G., Toro-Huamanchumo, C. J., & Hernández, A. V. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.727034 · Cited by 35
Public Perception of COVID-19 Vaccination in Italy: The Role of Trust and Experts’ Communication
Bucchi, M., Fattorini, E., & Saracino, B. (2022) · International Journal of Public Health · doi:10.3389/ijph.2022.1604222 · Cited by 27
Baghdadi, J. D., Coffey, K., Belcher, R., Frisbie, J., Hassan, N., Sim, D., & Malik, R. D. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad013 · Cited by 36
Taba, M., Ayre, J., Freeman, B., McCaffery, K. J., & Bonner, C. (2023) · Health Promotion International · doi:10.1093/heapro/daad034 · Cited by 27
Luo, J., Chen, Z., Liu, D., Li, H., He, S., Zeng, L., Yang, M., Liu, Z., Xiao, X., & Zhang, L. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01980-y · Cited by 27
The Demand and Supply of Pandemic Populism: A Global Overview
Zulianello, M., & Guasti, P. (2023) · Government and Opposition · doi:10.1017/gov.2023.35 · Cited by 27
Vince, J., Praet, E., Schofield, J., & Townsend, K. A. (2022) · Policy Sciences · doi:10.1007/s11077-022-09479-x · Cited by 26
Cross-platform social dynamics: an analysis of ChatGPT and COVID-19 vaccine conversations
Alipour, S., Galeazzi, A., Sangiorgio, E., Avalle, M., Bojić, L., Cinelli, M., & Quattrociocchi, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-53124-x · Cited by 33
Axelsson, A., Nygren, T., Roozenbeek, J., & van der Linden, S. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2338451 · Cited by 34
Lewis, C. C., Taba, M., Allen, T. B., Caldwell, P. H. Y., Skinner, S. R., Kang, M. S., Henderson, H., Bray, L., Borthwick, M., Collin, P. J., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/49453 · Cited by 26
Siam, M. H. B., Nishat, N. H., Ahmed, A., & Hossain, M. S. (2020) · Journal of Pathogens · doi:10.1155/2020/9121429 · Cited by 27
Science denial and medical misinformation in pandemic times: A psycho-criminological analysis
Lavorgna, A., & Myles, H. (2021) · European Journal of Criminology · doi:10.1177/1477370820988832 · Cited by 26
Khalafalla, H. E., Tumambeng, M. Z., Halawi, M. H. A., Masmali, E. M. A., Tashari, T. B. M., Arishi, F. H., Shadad, R. H. M., Alfaraj, S. Z. A., Fathi, S. M. A., & Mahfouz, M. S. (2022) · Vaccines · doi:10.3390/vaccines10020289 · Cited by 27
Suzuki, Y., Sukegawa, A., Ueda, Y., Sekine, M., Enomoto, T., Melamed, A., Wright, J. D., & Miyagi, E. (2022) · JMIR Public Health and Surveillance · doi:10.2196/34715 · Cited by 26
Acceptance of COVID-19 vaccine and determinant factors in the Iranian population: a web-based study
Omidvar, S., & Firouzbakht, M. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-07948-w · Cited by 27
Dadaczynski, K., Rathmann, K., Schricker, J., Bilz, L., Sudeck, G., Fischer, S. M., Janiczek, O., & Quilling, E. (2022) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-022-03548-5 · Cited by 26
Repeat Spreaders and Election Delegitimization
Kennedy, I. A., Wack, M., Beers, A. L., Schafer, J. S., Garcia-Camargo, I., Spiro, E. S., & Starbird, K. (2022) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2022.013 · Cited by 56
Metadata
- Authors
- Zarocostas, J.
- Corresponding author
- Zarocostas, John
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 395
- Issue
- 10225
- Pages
- 676
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7133615
- OpenAlex
- W3007198457
- Cited by
- 2,344
- References
- 0
- Citations by year
- 2026: 119 · 2025: 187 · 2024: 253 · 2023: 385 · 2022: 499 · 2021: 564 · 2020: 337
- FWCI
- 597.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Communication › Public Relations and Crisis Communication
- Keywords
- infodemic, COVID-19 misinformation, risk communication, social media, public health communication, rumors, health emergency preparedness, public trust in health information, public health emergency of international concern
- MeSH terms
- COVID-19, Humans, Pneumonia, Viral, Global Health, World Health Organization, Coronavirus Infections, Pandemics
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