Understanding the spread of COVID‐19 misinformation on social media: The effects of topics and a political leader's nudge
Open accessAbstract
The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID‐19 pandemic as a case study to systematically investigate factors associated with the spread of multi‐topic misinformation related to one event on social media based on the heuristic‐systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID‐19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · see citing works
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650 · see citing works
Nudge: improving decisions about health, wealth, and happiness
(2008) · Choice Reviews Online · doi:10.5860/choice.46-0977 · Cited by 11,614 · see citing works
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 6× · see citing works
“One reason is that people are more likely to share misinformation during crises (e.g., natural disasters, wars, terrorist attacks, virus breakouts) (Mosleh et al., 2020; Van Prooijen & Douglas, 2017; Vosoughi et al., 2018).”
BackgroundInfluentialSocial Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
Chaiken, S. (1980) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.39.5.752 · Cited by 5,516 · see citing works
Using social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964 · see citing works
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160 · see citing works
(1967) · Cited by 3,831 · see citing works
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304 · see citing works
Negative Binomial Regression: Index
Hilbe, J. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511973420 · Cited by 3,253 · see citing works
(2000) · Applied Physics Letters · doi:10.1162/jmlr.2003.3.4-5.993 · Cited by 3,030 · see citing works
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 2× · see citing works
“Sharing information (e.g., retweeting) is a key feature provided by social media sites such as Twitter (Cha et al., 2010; Suh et al., 2010).”
BackgroundExploring the Space of Topic Coherence Measures
Röder, M., Both, A., & Hinneburg, A. (2015) · Web Search and Data Mining · doi:10.1145/2684822.2685324 · Cited by 2,501 · see citing works
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407 · see citing works
The COVID-19 social media infodemic
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-73510-5 · Cited by 2,313 · see citing works
The limited capacity model of mediated message processing
Lang, A. (2000) · Journal of Communication · doi:10.1111/j.1460-2466.2000.tb02833.x · Cited by 2,153 · cited 1× · see citing works
“Such situations can increase people's reliance on the heuristic mode (Eastin, 2001; Lang, 2000; Ratneshwar & Chaiken, 1991; Zuckerman & Chaiken, 1998) when they process and share information related to COVID-19.”
BackgroundSystematic Literature Review on the Spread of Health-related Misinformation on Social Media
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112552 · Cited by 1,806 · see citing works
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803 · see citing works
Less than you think: Prevalence and predictors of fake news dissemination on Facebook
Guess, A., Nagler, J., & Tucker, J. (2019) · Science Advances · doi:10.1126/sciadv.aau4586 · Cited by 1,652 · see citing works
The role of social networks in information diffusion
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187907 · Cited by 1,526 · see citing works
Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task.
Bond, R., & Smith, P. B. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.111 · Cited by 1,486 · see citing works
Susceptibility to misinformation about COVID-19 around the world
Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., & van der Linden, S. (2020) · Royal Society Open Science · doi:10.1098/rsos.201199 · Cited by 1,447 · see citing works
Presidential Popularity from Truman to Johnson
Mueller, J. E. (1970) · American Political Science Review · doi:10.2307/1955610 · Cited by 1,311 · see citing works
Prior exposure increases perceived accuracy of fake news
Pennycook, G., Cannon, T. D., & Rand, D. G. (2018) · Journal of Experimental Psychology General · doi:10.1037/xge0000465 · Cited by 1,268 · cited 1× · see citing works
“In fact, even a single prior encounter can increase the believability of information (Pennycook et al., 2018).”
BackgroundChaiken, S., & Maheswaran, D. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.66.3.460 · Cited by 1,214 · see citing works
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
Suh, B., Hong, L., Pirolli, P. L. T., & Ed, H. (2010) · doi:10.1109/socialcom.2010.33 · Cited by 1,183 · see citing works
News use across social media platforms 2016
Gottfried, J. A., & Shearer, E. (2016) · Cited by 1,165 · see citing works
Believe it or not: Factors influencing credibility on the Web
Wathen, C. N., & Burkell, J. (2001) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.10016 · Cited by 1,093 · see citing works
The heuristic model of persuasion.
Chaiken, S. (1987) · Cited by 1,076 · see citing works
Chaiken, S., & Maheswaran, D. (1994) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.66.3.460 · Cited by 1,047 · see citing works
Conditional automaticity: Varieties of automatic influence in social perception and cognition.
Bargh, J. A. (1989) · Cited by 975 · see citing works
Science audiences, misinformation, and fake news
Scheufele, D. A., & Krause, N. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805871115 · Cited by 878 · see citing works
Automatic evaluation of topic coherence
Newman, D. J., Lau, J. H., Grieser, K., & Baldwin, T. (2010) · Cited by 847 · see citing works
Conspiracy theories as part of history: The role of societal crisis situations
van Prooijen, J.-W., & Douglas, K. M. (2017) · Memory Studies · doi:10.1177/1750698017701615 · Cited by 764 · see citing works
Impact of Rumors and Misinformation on COVID-19 in Social Media
Tasnim, S., Hossain, M. M., & Mazumder, H. (2020) · Journal of Preventive Medicine and Public Health · doi:10.3961/jpmph.20.094 · Cited by 729 · see citing works
Trends in the diffusion of misinformation on social media
Allcott, H., Gentzkow, M. A., & Yu, C. (2019) · Research & Politics · doi:10.1177/2053168019848554 · Cited by 680 · see citing works
Science vs Conspiracy: Collective Narratives in the Age of Misinformation
Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118093 · Cited by 645 · see citing works
Modeling compliance with COVID-19 prevention guidelines: the critical role of trust in science
Plohl, N., & Musil, B. (2020) · Psychology Health & Medicine · doi:10.1080/13548506.2020.1772988 · Cited by 553 · see citing works
Evaluating the fake news problem at the scale of the information ecosystem
Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020) · Science Advances · doi:10.1126/sciadv.aay3539 · Cited by 528 · see citing works
Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514 · see citing works
Self-citationFactors affecting YouTube influencer marketing credibility: a heuristic-systematic model
Xiao, M., Wang, R., & Chan-Olmsted, S. (2018) · Journal of Media Business Studies · doi:10.1080/16522354.2018.1501146 · Cited by 497 · see citing works
Anatomy of a Rally Effect: George W. Bush and the War on Terrorism
Hetherington, M. J., & Nelson, M. B. (2003) · PS Political Science & Politics · doi:10.1017/s1049096503001665 · Cited by 450 · see citing works
Misinformation in Social Media: Definition, Manipulation, and Detection
Wu, L., Morstatter, F., Carley, K. M., et al. (2019) · SKDD · doi:10.1145/3373464.3373475 · Cited by 424 · see citing works
Seeking Better Health Care Outcomes: The Ethics of Using the “Nudge”
Blumenthal-Barby, J. S. S., & Burroughs, H. R. (2012) · The American Journal of Bioethics · doi:10.1080/15265161.2011.634481 · Cited by 413 · see citing works
Bakhshi, S., Shamma, D. A., & Gilbert, É. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557403 · Cited by 401 · see citing works
Eastin, M. S. (2001) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2001.tb00126.x · Cited by 364 · see citing works
Ratneshwar, S., & Chaiken, S. (1991) · Journal of Consumer Research · doi:10.1086/209240 · Cited by 355 · see citing works
Turning a Shove into a Nudge? A “Labeled Cash Transfer” for Education
Benhassine, N., Devoto, F., Duflo, E., Pascaline Dupas, Y., & Pouliquen, V. (2015) · American Economic Journal Economic Policy · doi:10.1257/pol.20130225 · Cited by 352 · see citing works
The influentials: people who influence people
(1995) · Choice Reviews Online · doi:10.5860/choice.32-4316 · Cited by 342 · see citing works
Chen, S., Shechter, D., & Chaiken, S. (1996) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.71.2.262 · Cited by 340 · see citing works
Kim, A., Moravec, P. L., & Dennis, A. R. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1628921 · Cited by 323 · see citing works
Ross, A. S., & Rivers, D. J. (2018) · Social Media + Society · doi:10.1177/2056305118776010 · Cited by 319 · see citing works
World leaders' usage of twitter in response to the COVID-19 pandemic: A content analysis
Rufai, S. R., & Bunce, C. (2020) · Journal of Public Health · doi:10.1093/pubmed/fdaa049 · Cited by 308 · see citing works
Hahl, O., Kim, M., & Zuckerman, E. W. (2018) · American Sociological Review · doi:10.1177/0003122417749632 · Cited by 305 · see citing works
Georgiou, N., Delfabbro, P., & Balzan, R. P. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2020.110201 · Cited by 291 · see citing works
Parmelee, J. H., & Bichard, S. L. (2011) · Cited by 275 · see citing works
The Interplay of Heuristic and Systematic Processing of Social Information
Bohner, G., Moskowitz, G. B., & Chaiken, S. (1995) · European Review of Social Psychology · doi:10.1080/14792779443000003 · Cited by 267 · see citing works
Salvaging the concept of nudge
Saghai, Y. (2013) · Journal of Medical Ethics · doi:10.1136/medethics-2012-100727 · Cited by 264 · see citing works
The Multiple Facets of Influence
Dubois, E., & Gaffney, D. (2014) · American Behavioral Scientist · doi:10.1177/0002764214527088 · Cited by 263 · see citing works
Outtweeting the twitterers - predicting information cascades in microblogs
Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., & Kellerer, W. (2010) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 251 · see citing works
A first look at COVID-19 information and misinformation sharing on TwitterPreprint
Singh, L., Bansal, S., Bode, L., Budak, C., Chi, G., Kawintiranon, K., Padden, C., Vanarsdall, R., Vraga, E. K., & Wang, Y. (2020) · PubMed · doi:10.48550/arxiv.2003.13907 · Cited by 249 · see citing works
Detecting misinformation in online social networks using cognitive psychology
Kumar, K. P. K., & Geethakumari, G. (2014) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-014-0014-x · Cited by 242 · see citing works
Is boredom proneness related to social media overload and fatigue? A stress–strain–outcome approach
Whelan, E., Islam, A. K. M. N., & Brooks, S. L. (2020) · Internet Research · doi:10.1108/intr-03-2019-0112 · Cited by 239 · see citing works
Bækgaard, M., Christensen, J., Madsen, J. K., & Mikkelsen, K. S. (2020) · Journal of Behavioral Public Administration · doi:10.30636/jbpa.32.172 · Cited by 236 · see citing works
The “Tweet Politics” of President Trump
Kreis, R. (2017) · Journal of Language and Politics · doi:10.1075/jlp.17032.kre · Cited by 226 · see citing works
Information Overload and Infodemic in the COVID-19 Pandemic
Rathore, F. A., & Farooq, F. (2020) · Journal of the Pakistan Medical Association · doi:10.5455/jpma.38 · Cited by 218 · see citing works
Social media and credibility indicators: The effect of influence cues
Lin, X., Spence, P. R., & Lachlan, K. A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.002 · Cited by 216 · see citing works
The Use of Social Media in Healthcare: Organizational, Clinical, and Patient Perspectives
Househ, M. S. (2013) · Studies in health technology and informatics · doi:10.3233/978-1-61499-203-5-244 · Cited by 212 · see citing works
Fake News: A Survey of Research, Detection Methods, and Opportunities.Preprint
Zhou, X., & Zafarani, R. (2018) · arXiv (Cornell University) · Cited by 212 · see citing works
Understanding Fake News Consumption: A Review
Baptista, J. P., & Gradim, A. (2020) · Social Sciences · doi:10.3390/socsci9100185 · Cited by 202 · see citing works
Determinants of information retweeting in microblogging
Liu, Z., Liu, L., & Li, H. (2012) · Internet Research · doi:10.1108/10662241211250980 · Cited by 200 · see citing works
Riedel, R., Addo, R., Ferreira‐Gomes, M., Heinz, G. A., Heinrich, F., Kummer, J., Greiff, V., Schulz, D., Klaeden, C., Cornelis, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16464-6 · Cited by 200 · see citing works
Mosleh, M., Pennycook, G., & Rand, D. G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228882 · Cited by 172 · see citing works
Earp (2019) · doi:10.1098/rsos.190161 · Cited by 160 · see citing works
Fact-checking Effect on Viral Hoaxes
Tambuscio, M., Ruffo, G., Flammini, A., & Menczer, F. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742572 · Cited by 158 · see citing works
Social media’s contribution to political misperceptions in U.S. Presidential elections
Garrett, R. K. (2019) · PLOS ONE · doi:10.1371/journal.pone.0213500 · Cited by 151 · see citing works
Combining interventions to reduce the spread of viral misinformation
Bak-Coleman, J. B., Kennedy, I., Wack, M., Beers, A., Schafer, J. S., Spiro, E. S., Starbird, K., & West, J. D. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01388-6 · Cited by 145 · see citing works
Propagating and Debunking Conspiracy Theories on Twitter During the 2015–2016 Zika Virus Outbreak
Wood, M. J. (2018) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2017.0669 · Cited by 143 · see citing works
A heuristic-systematic processing analysis of the effectiveness of product warning labels
Zuckerman, A., & Chaiken, S. (1998) · Psychology and Marketing · doi:10.1002/(sici)1520-6793(199810)15:7 · Cited by 140 · see citing works
Using the president’s tweets to understand political diversion in the age of social media
Lewandowsky, S., Jetter, M., & Ecker, U. K. H. (2020) · Nature Communications · doi:10.1038/s41467-020-19644-6 · Cited by 139 · see citing works
Gainous, J., & Wagner, K. M. (2013) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199965076.001.0001 · Cited by 134 · see citing works
Fung, I. C., Fu, K., Chan, C., Chan, B. S. B., Cheung, C.-N., Abraham, T., & Tse, Z. T. H. (2016) · Public Health Reports · doi:10.1177/003335491613100312 · Cited by 129 · see citing works
Content or context: Which matters more in information processing on microblogging sites
Zhang, L., Peng, T., Zhang, Y.-P., Wang, X.-H., & Zhu, J. J. H. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.10.031 · Cited by 125 · see citing works
Self-citationEvaluation nudge: Effect of evaluation mode of online customer reviews on consumers’ preferences
Tan, H., Lv, X., Liu, X., & Gürsoy, D. (2017) · Tourism Management · doi:10.1016/j.tourman.2017.09.011 · Cited by 116 · see citing works
The Feeling of Familiarity as a Regulator of Persuasive Processing
Garcia‐Marques, T., & Mackie, D. M. (2001) · Social Cognition · doi:10.1521/soco.19.1.9.18959 · Cited by 114 · see citing works
Finding influential users of online health communities: a new metric based on sentiment influence
Zhao, K., Yen, J., Greer, G. E., Qiu, B., Mitra, P., & Portier, K. M. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002282 · Cited by 112 · see citing works
Self-citationSchmidt, A. T. (2017) · American Political Science Review · doi:10.1017/s0003055417000028 · Cited by 107 · see citing works
Fu, W., & Sim, C. (2011) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21641 · Cited by 97 · see citing works
Geographic Diversification and Banks’ Funding Costs
Levine, R. E., Lin, C., & Xie, W. (2020) · Management Science · doi:10.1287/mnsc.2020.3582 · Cited by 93 · see citing works
Flexible context aware interface for ambient assisted living
McNaull, J., Augusto, J. C., Mulvenna, M. D., et al. (2014) · Human-centric Computing and Information Sciences · doi:10.1186/2192-1962-4-1 · Cited by 88 · see citing works
Misinformation Spreading on Facebook
Zollo, F., & Quattrociocchi, W. (2018) · Computational social sciences · doi:10.1007/978-3-319-77332-2_10 · Cited by 75 · 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
Coronavirus on Social Media: Analyzing Misinformation in Twitter ConversationsPreprint
Sharma, K., Seo, S., Meng, C.-Z., Rambhatla, S., Dua, A., & Liu, Y. (2020) · arXiv.org · doi:10.48550/arxiv.2003.12309 · Cited by 67 · see citing works
Son, J., Lee, J., Oh, O., Lee, H. K., & Woo, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102176 · Cited by 66 · see citing works
Butler, D. M., Vries, C. E. D., & Solaz, H. (2019) · Research & Politics · doi:10.1177/2053168019891619 · Cited by 63 · see citing works
Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphsPreprint
Ordun, C. Y., Purushotham, S., & Raff, E. (2020) · arXiv (Cornell University) · doi:10.13016/m24rdv-a0gg · Cited by 61 · see citing works
Retweet: A popular information diffusion mechanism – A survey paper
Firdaus, S. N., Ding, C., & Sadeghian, A. (2018) · Online Social Networks and Media · doi:10.1016/j.osnem.2018.04.001 · Cited by 60 · see citing works
Sharing Information on COVID-19: the ethical challenges in the Malaysian setting
Yusof, A. N. M., Muuti, M. Z., Ariffin, L. A., & Tan, M. K. M. (2020) · Asian Bioethics Review · doi:10.1007/s41649-020-00132-4 · Cited by 58 · see citing works
Ennals, R., Byler, D., Agosta, J. M., & Rosario, B. (2010) · doi:10.1145/1772938.1772952 · Cited by 50 · see citing works
Hemsley, J. (2019) · Policy & Internet · doi:10.1002/poi3.202 · Cited by 43 · see citing works
Ali, K. I., & Zain-ul-abdin, K. (2020) · Journal of Applied Communication Research · doi:10.1080/00909882.2020.1847311 · Cited by 43 · see citing works
Holz, J. E., List, J. A., Zentner, A., et al. (2020) · National Bureau of Economic Research · doi:10.3386/w27666 · Cited by 39 · see citing works
Combining interventions to reduce the spread of viral misinformationPreprint
Bak-Coleman, J. B., Kennedy, I., Wack, M., Beers, A. L., Schafer, J. S., Spiro, E. S., Starbird, K., & West, J. D. (2021) · doi:10.31235/osf.io/4jtvm · Cited by 31 · see citing works
Public preferences regarding police facebook posts: a macro-level analysis
Hu, X., Rodgers, K., & Lovrich, N. P. (2018) · Police Practice and Research · doi:10.1080/15614263.2018.1531753 · Cited by 26 · see citing works
Commentaries on Brunner and Willard’s ‘professional insecurities’
(2003) · Policy Sciences · doi:10.1023/a:1022908232719 · Cited by 17 · see citing works
Zhu, W., Norman, S. M., Peng, Z., Riggio, R. E., & Sosik, J. J. (2012) · Leadership · doi:10.1177/1742715011434745 · Cited by 16 · see citing works
Engelmann, I., Kloß, A., Neuberger, C., & Brockmann, T. H. (2019) · International journal of communication · Cited by 13 · see citing works
Social Media and Political Contributions: The Impact of New Technology on Political Competition
Petrova, M., Sen, A., & Yildirim, P. (2020) · Management Sciences · doi:10.1287/mnsc.2020.3740 · Cited by 8 · see citing works
Cummings Christopher, L., & Yi, K. W. (2019) · NATO science for peace and security series. D, Information and communication security · doi:10.3233/nicsp190032 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Lancet Commission on lessons for the future from the COVID-19 pandemic
Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
- Authors
- Wang, X., Zhang, M., Fan, W., & Zhao, K.
- Affiliations
- University of Iowa (US)
- Corresponding author
- Fan, Weiguo
- Zhao, Kang
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2021-09-27
- Volume
- 73
- Issue
- 5
- Pages
- 726-737
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- PMC
- PMC8653058
- OpenAlex
- W3201762888
- Cited by
- 67
- References
- 113
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 10 · 2023: 23 · 2022: 14 · 2021: 2
- FWCI
- 16.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Association for Information Science and Technology (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
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- COVID-19 misinformation, social media, misinformation spread, conspiracy theories, heuristic-systematic model, political leaders, Donald Trump, nudging, retweeting behavior, information sharing, crisis communication
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
