The DOI matches its Crossref record; 38 of the 41 references it cites were found in a registry or an index, and 3 are not checked yet.
Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention
Open accessAbstract
Across two studies with more than 1,700 U.S. adults recruited online, we present evidence that people share false claims about COVID-19 partly because they simply fail to think sufficiently about whether or not the content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they were asked directly about accuracy. Furthermore, greater cognitive reflection and science knowledge were associated with stronger discernment. In Study 2, we found that a simple accuracy reminder at the beginning of the study (i.e., judging the accuracy of a non-COVID-19-related headline) nearly tripled the level of truth discernment in participants’ subsequent sharing intentions. Our results, which mirror those found previously for political fake news, suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media.
Overview
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
Figures
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [23]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Experimental work indicates that directing users' attention to accuracy can improve sharing quality and reduce misinformation circulation (Pennycook et al., 2020(Pennycook et al., , 2021;;Roozenbeek et al., 2021).”
From Sensational Headlines, Engagement, and Credibility in Nepal’s Online News (2025) · cites this paper as Pennycook et al., 2020
The cited source says what the claim attributes to it.
In the citing paper
“Pennycook et al. (2020) and Roozenbeek et al. (2021) show that accuracy prompts can modestly improve users' sharing decisions, while Pennycook et al. (2021) argue that shifting attention toward accuracy can reduce misinformation sharing online.”
From Sensational Headlines, Engagement, and Credibility in Nepal’s Online News (2025) · cites this paper as Pennycook et al. (2020)
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Consistent with this view, a growing number of empirical studies have uncovered evidence that people update their political attitudes and beliefs in line with evidence (e.g., refs. 4,13,27–29) and arguments23,30,31 presented to them.”
From Factual knowledge can reduce attitude polarization (2025) · cites this paper as [28]
Experimental — accuracy not yet measured
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
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022 · cited 1×
“A great deal of evidence suggests that people are motivated to seek out, believe, and share politically congenial information (Kahan, Peters, Dawson, & Slovic, 2017; Kunda, 1990; Lee, Shin, & Hong, 2018; Mercier & Sperber, 2011; Shin & Thorson, 2017).”
BackgroundCognitive Reflection and Decision Making
Frederick, S. (2005) · Journal of Economic Perspectives · doi:10.1257/089533005775196732 · Cited by 5,256
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
Self-citationWhy do humans reason? Arguments for an argumentative theory
Mercier, H., & Sperber, D. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000968 · Cited by 2,254
Pennycook, G., & Rand, D. G. (2019) · Cognition · doi:10.1016/j.cognition.2018.06.011 · Cited by 2,022
Self-citationShifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155
Self-citationThe Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks
Toplak, M. E., West, R. F., & Stanovich, K. E. (2011) · Memory & Cognition · doi:10.3758/s13421-011-0104-1 · Cited by 1,047
Coppock, A., & McClellan, O. A. (2019) · Research & Politics · doi:10.1177/2053168018822174 · Cited by 987
Berinsky, A. J., Margolis, M. F., & Sances, M. W. (2014) · American Journal of Political Science · doi:10.1111/ajps.12081 · Cited by 951
Fighting misinformation on social media using crowdsourced judgments of news source quality
Pennycook, G., & Rand, D. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806781116 · Cited by 947 · cited 4×
“…2017; Van Bavel & Pereira, 2018), our data are more consistent with recent studies on political misinformation that provide both correlational (Pennycook & Rand, 2019b; including data from Twitter sharing, Mosleh, Pennycook, Arechar, & Rand, 2020) and experimental (Bago, Rand, & Pennycook,…”
BackgroundResultInfluentialSelf-citation
Research works printed but not linked: 2
- Adhanom Ghebreyesus T. (2020, February 15). Munich Security Conference. World Health Organization. Retrieved from https://www.who.int/dg/speeches/detail/munich-security-conference
- Ross R. M., 2019, PsyArXiv
Cited by
Sorted by citations
Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01056-1 · Cited by 1,800
Functional Fear Predicts Public Health Compliance in the COVID-19 Pandemic
Harper, C. A., Satchell, L. P., Fido, D., & Latzman, R. D. (2021) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-020-00281-5 · Cited by 1,564
The psychological drivers of misinformation belief and its resistance to correction
Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-021-00006-y · Cited by 1,372
Pennycook, G., & Rand, D. G. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.02.007 · Cited by 1,360
“For example, participants who were asked about the accuracy of a set of headlines rated true headlines as much more accurate than false headlines; but, when asked whether they would share the headlines, veracity had little impact on sharing intentions – both in the context of political headlines [21] (Figure 2A) and h…”
BackgroundInfluentialSelf-citationPuri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2020.1780846 · Cited by 1,229
Shifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155
Self-citationPolitical Sectarianism in America
Finkel, E. J., Bail, C. A., Cikara, M., Ditto, P. H., Iyengar, S., Klar, S., Mason, L., McGrath, M. C., Nyhan, B., Rand, D. G., et al. (2020) · Science · doi:10.1126/science.abe1715 · Cited by 931
Self-citationFake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772
Fake news and COVID-19: modelling the predictors of fake news sharing among social media users
Apuke, O. D., & Omar, B. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101475 · Cited by 832
OSMUNDSEN, M., BOR, A., VAHLSTRUP, P. B., BECHMANN, A., & PETERSEN, M. B. (2021) · American Political Science Review · doi:10.1017/s0003055421000290 · Cited by 614
Misinformation: susceptibility, spread, and interventions to immunize the public
van der Linden, S. (2022) · Nature Medicine · doi:10.1038/s41591-022-01713-6 · Cited by 570
Toward effective government communication strategies in the era of COVID-19
Hyland, B., Gardner, J., Leask, J., & Ecker, U. K. H. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00701-w · Cited by 536
Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools
Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020) · Psychological Science in the Public Interest · doi:10.1177/1529100620946707 · Cited by 479
Chater, N., & Loewenstein, G. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002023 · Cited by 710
Psychological inoculation improves resilience against misinformation on social media
Roozenbeek, J., van der Linden, S., Goldberg, B., Rathje, S., & Lewandowsky, S. (2022) · Science Advances · doi:10.1126/sciadv.abo6254 · Cited by 472
Fagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., & Huiart, L. (2020) · Journal of Medical Internet Research · doi:10.2196/19284 · Cited by 426
Reliance on emotion promotes belief in fake news
Martel, C., Pennycook, G., & Rand, D. G. (2020) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-020-00252-3 · Cited by 470
Self-citationSu, Z., McDonnell, D., Wen, J., Kozak, M., Abbas, J., Šegalo, S., Li, X. A., Aḥmad, J., Cheshmehzangi, A., Cai, Y., et al. (2021) · Globalization and Health · doi:10.1186/s12992-020-00654-4 · Cited by 424
Barua, Z., Barua, S., Aktar, S., Kabir, N., & Li, M. (2020) · Progress in Disaster Science · doi:10.1016/j.pdisas.2020.100119 · Cited by 415
Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106754 · Cited by 375
AI and the transformation of social science research
Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023) · Science · doi:10.1126/science.adi1778 · Cited by 377
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
Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review
van Mulukom, V., Pummerer, L., Alper, S., Bai, H., Čavojová, V., Farias, J. E. M., Kay, C. S., Lazarević, L. B., Lobato, E. J. C., Marinthe, G., et al. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114912 · Cited by 437
Lee, J. J., Kang, K., Wang, M. P., Zhao, S. Z., Wong, J. Y. H., O’Connor, S., Yang, S. C., & Shin, S. H. (2020) · Journal of Medical Internet Research · doi:10.2196/22205 · Cited by 327
Fake news on Social Media: the Impact on Society
Olan, F., Jayawickrama, U., Arakpogun, E. O., Suklan, J., & Liu, S. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10242-z · Cited by 396
Unger, S. D., & Meiran, W. (2020) · International Journal of Technology in Education and Science · doi:10.46328/ijtes.v4i4.107 · Cited by 314
Vaccine Hesitancy: Contemporary Issues and Historical Background
Nuwarda, R. F., Ramzan, I. M., Weekes, L., & Kayser, V. (2022) · Vaccines · doi:10.3390/vaccines10101595 · Cited by 341
Using artificial intelligence to improve public health: a narrative review
Olawade, D. B., Wada, O. Z., David-Olawade, A. C., Kunonga, E., Abaire, O. J., & Ling, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1196397 · Cited by 341
Basol, M., Roozenbeek, J., Berriche, M., Uenal, F., McClanahan, W. P., & Linden, S. V. D. (2021) · Big Data & Society · doi:10.1177/20539517211013868 · Cited by 271
COVID-19 open source data sets: a comprehensive survey
Shuja, J., Alanazi, E., Alasmary, W., & Alashaikh, A. S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01862-6 · Cited by 278
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Gareth Tyson, A., Castro, I., Razi, A., Boulos, M. N. K., Weller, A., et al. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2020.3020521 · Cited by 272
Fridman, I., Lucas, N. R., Henke, D. M., & Zigler, C. K. (2020) · JMIR Public Health and Surveillance · doi:10.2196/22060 · Cited by 264
Pennycook, G., & Rand, D. G. (2022) · Nature Communications · doi:10.1038/s41467-022-30073-5 · Cited by 270
Self-citationAli, S. H., Foreman, J., Tozan, Y., Capasso, A., Jones, A. M., & DiClemente, R. J. (2020) · JMIR Public Health and Surveillance · doi:10.2196/21071 · Cited by 252
Why do so few people share fake news? It hurts their reputation
Altay, S., Hacquin, A.-S., & Mercier, H. (2020) · New Media & Society · doi:10.1177/1461444820969893 · Cited by 271
Bermes, A. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102555 · Cited by 264
Rural and Urban Differences in COVID‐19 Prevention Behaviors
Callaghan, T., Lueck, J. A., Trujillo, K. L., & Ferdinand, A. O. (2021) · The Journal of Rural Health · doi:10.1111/jrh.12556 · Cited by 227
Bryanov, K., & Vziatysheva, V. (2021) · PLOS ONE · doi:10.1371/journal.pone.0253717 · Cited by 211
COVIDLies: Detecting COVID-19 Misinformation on Social Media
Hossain, T., Logan, R. L., Ugarte, D. A., Matsubara, Y., Young, S. D., & Singh, S. (2020) · Workshop on NLP for COVID-19 (NLP-COVID19) · doi:10.18653/v1/2020.nlpcovid19-2.11 · Cited by 212
Nan, X., Wang, Y., & Thier, K. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115398 · Cited by 247
Political Psychology in the Digital (mis)Information age: A Model of News Belief and Sharing
Van Bavel, J. J., Harris, E. A., Pärnamets, P., Rathje, S., Doell, K. C., & Tucker, J. A. (2021) · Social Issues and Policy Review · doi:10.1111/sipr.12077 · Cited by 213
COVID-19 Misinformation Trends in Australia: Prospective Longitudinal National Survey
Pickles, K. M., Cvejic, E., Nickel, B., Copp, T., Bonner, C., Leask, J., Ayre, J., Batcup, C., Cornell, S., Dakin, T., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/23805 · Cited by 206
Scaling up fact-checking using the wisdom of crowds
Allen, J., Arechar, A. A., Pennycook, G., & Rand, D. G. (2021) · Science Advances · doi:10.1126/sciadv.abf4393 · Cited by 234
“…with rapidly evolving facts, such as in the case of the coronavirus disease 2019 (COVID-19) news environment, results for both the crowd and fact-checkers would differ [although studies with researcher-selected true and false COVID-19 headlines find high crowd performance, e.g., ( 39 )].”
Self-citationAdjin-Tettey, T. D. (2022) · Cogent Arts and Humanities · doi:10.1080/23311983.2022.2037229 · Cited by 296
Oleksy, T., Wnuk, A., Maison, D. A., & Łyś, A. E. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2020.110289 · Cited by 200
Spread of misinformation on social media: What contributes to it and how to combat it
Chen, S., Xiao, L., & Kumar, A. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107643 · Cited by 239
Changes in Consumption Patterns and Tourist Promotion after the COVID-19 Pandemic
Muñiz, D. R.-T., Vila, N. A., & Brea, J. A. F. (2021) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer16050075 · Cited by 181
Sallam, M., Dababseh, D., Yaseen, A. I. B., Al-Haidar, A., Taim, D., Eid, H., Ababneh, N. A., Bakri, F. G., & Mahafzah, A. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243264 · Cited by 181
Pian, W., Chi, J., & Ma, F. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102713 · Cited by 198
Cognitive reflection correlates with behavior on Twitter
Mosleh, M., Pennycook, G., Arechar, A. A., & Rand, D. G. (2021) · Nature Communications · doi:10.1038/s41467-020-20043-0 · Cited by 175
Self-citationPolitical polarization drives online conversations about COVID ‐19 in the United States
Jiang, J., Chen, E., Shen, Y., Lerman, K., & Ferrara, E. (2020) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.202 · Cited by 193
Fake News and Covid-19 in Italy: Results of a Quantitative Observational Study
Moscadelli, A., Albora, G., Biamonte, M. A., Giorgetti, D., Innocenzio, M., Paoli, S., Lorini, C., Bonanni, P., & Bonaccorsi, G. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17165850 · Cited by 186
Sternisko, A., Cichocka, A. K., Cisłak, A., & Bavel, J. J. V. (2021) · Personality and Social Psychology Bulletin · doi:10.1177/01461672211054947 · Cited by 199
Understanding and combatting misinformation across 16 countries on six continents
Arechar, A. A., Allen, J., Berinsky, A. J., Cole, R., Epstein, Z., Garimella, K., Gully, A., Lu, J. G., Ross, R. M., Stagnaro, M. N., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01641-6 · Cited by 188
Self-citationThe use of medicinal plants to prevent COVID-19 in Nepal
Khadka, D., Dhamala, M. K., Li, F., Aryal, P. C., Magar, P. R., Bhatta, S., Thakur, M. S., Basnet, A., Cui, D., & Shi, S. (2021) · Journal of Ethnobiology and Ethnomedicine · doi:10.1186/s13002-021-00449-w · Cited by 163
Fighting fake news in the COVID-19 era: policy insights from an equilibrium model
Hartley, K., & Vu, M. K. (2020) · Policy Sciences · doi:10.1007/s11077-020-09405-z · Cited by 175
Sharing of misinformation is habitual, not just lazy or biased
Ceylan, G., Anderson, I. A., & Wood, W. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2216614120 · Cited by 195
Beyond (Mis)Representation: Visuals in COVID-19 Misinformation
Brennen, J. S., Simon, F. M., & Nielsen, R. K. (2021) · The International Journal of Press/Politics · doi:10.1177/1940161220964780 · Cited by 179
Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter
Al-Rakhami, M. S., & Alamri, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3019600 · Cited by 153
Quantifying the effects of fake news on behavior: Evidence from a study of COVID-19 misinformation.
Greene, C. M., & Murphy, G. (2021) · Journal of Experimental Psychology Applied · doi:10.1037/xap0000371 · Cited by 164
Zettler, I., Schild, C., Lilleholt, L., Kroencke, L., Utesch, T., Moshagen, M., BOHM, R. M., Back, M. D., & Geukes, K. (2021) · Social Psychological and Personality Science · doi:10.1177/19485506211001680 · Cited by 165
What types of COVID-19 conspiracies are populated by Twitter bots?
Ferrara, E. (2020) · First Monday · doi:10.5210/fm.v25i6.10633 · Cited by 223
“The work by Pennycook and colleagues epitomizes the seriousness of this problem by showing, with a social experiment including 1,600 participants, that subjects tend to share misinformation and false claims about COVID-19 predominantly because they are unable to determine whether the content is scientifically sound an…”
BackgroundCodagnone, C., Bogliacino, F., Gómez, C. E., Charris, R., Montealegre, F., Liva, G., Lupiáñez‐Villanueva, F., Folkvord, F., & Veltri, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240876 · Cited by 148
Xiao, X., Borah, P., & Su, Y. (2021) · Public Understanding of Science · doi:10.1177/0963662521998025 · Cited by 175
Breaking Harmony Square: A game that “inoculates” against political misinformation
Roozenbeek, J., & van der Linden, S. (2020) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-47 · Cited by 171
Zhou, C., Xiu, H., Wang, Y., & Yu, X. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102554 · Cited by 136
Apuke, O. D., & Omar, B. (2020) · Online Information Review · doi:10.1108/oir-03-2020-0116 · Cited by 153
A meta-analysis of correction effects in science-relevant misinformation
Chan, M. S., & Albarracín, D. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01623-8 · Cited by 154
Nagler, R. H., Vogel, R. I., Gollust, S. E., Rothman, A. J., Fowler, E. F., & Yzer, M. C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240776 · Cited by 134
Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges
de Oliveira, N. R., Pisa, P. S., Lopez, M. A., Medeiros, D. S. V., & Mattos, D. M. F. (2021) · Information · doi:10.3390/info12010038 · Cited by 149
(Why) Is Misinformation a Problem?
Adams, Z., Osman, M., Bechlivanidis, C., & Meder, B. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221141344 · Cited by 182
How to think about whether misinformation interventions work
Guay, B., Berinsky, A. J., Pennycook, G., & Rand, D. G. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01667-w · Cited by 138
Self-citationVaccination strategies against COVID-19 and the diffusion of anti-vaccination views
Prieto-Curiel, R., & Ramírez, H. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-85555-1 · Cited by 135
Apuke, O. D., & Omar, B. (2021) · Health Informatics Journal · doi:10.1177/14604582211021470 · Cited by 145
Roozenbeek, J., Culloty, E., & Suiter, J. (2023) · European Psychologist · doi:10.1027/1016-9040/a000492 · Cited by 150
News from Generative Artificial Intelligence Is Believed Less
Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533077 · Cited by 161
Self-citationA Framework for the Study of Persuasion
Druckman, J. N. (2022) · Annual Review of Political Science · doi:10.1146/annurev-polisci-051120-110428 · Cited by 126
Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines
Ross, R. M., Rand, D. G., & Pennycook, G. (2021) · Judgment and Decision Making · doi:10.1017/s1930297500008640 · Cited by 132
Self-citationRoozenbeek, J., Freeman, A. L. J., & van der Linden, S. (2021) · Psychological Science · doi:10.1177/09567976211024535 · Cited by 121
The COVID-19 Misinfodemic: Moving Beyond Fact-Checking
Chou, W. S., Gaysynsky, A., & Vanderpool, R. C. (2020) · Health Education & Behavior · doi:10.1177/1090198120980675 · Cited by 123
van der Meer, T. G. L. A., Hameleers, M., & Ohme, J. (2023) · Journalism Studies · doi:10.1080/1461670x.2023.2187652 · Cited by 121
COVID-19-Related Social Media Fake News in India
Al-Zaman, M. S. (2021) · Journalism and Media · doi:10.3390/journalmedia2010007 · Cited by 139
Rovetta, A. (2021) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2021.670226 · Cited by 144
Online searches to evaluate misinformation can increase its perceived veracity
Aslett, K., Sanderson, Z., Godel, W., Persily, N., Nagler, J., & Tucker, J. A. (2023) · Nature · doi:10.1038/s41586-023-06883-y · Cited by 151
Jahanbakhsh, F., Zhang, A. X., Berinsky, A. J., Pennycook, G., Rand, D. G., & Karger, D. R. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449092 · Cited by 112
Self-citationConway, L. G., Woodard, S. R., Zubrod, A., & Chan, L. (2021) · Personality and Individual Differences · doi:10.1016/j.paid.2021.111124 · Cited by 123
Belief in Science Influences Physical Distancing in Response to COVID-19 Lockdown PoliciesPreprint
Brzezinski, A., Kecht, V., Dijcke, D. V., & Wright, A. L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3587990 · Cited by 115
Maertens, R., Götz, F. M., Golino, H. F., Roozenbeek, J., Schneider, C. R., Kyrychenko, Y., Kerr, J. R., Stieger, S., McClanahan, W. P., III, Drabot, K., et al. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02124-2 · Cited by 161
Mosleh, M., Martel, C., Eckles, D., & Rand, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445642 · Cited by 106
Self-citationWong, F. H. C., Liu, T., Leung, D. K. Y., Zhang, A. Y., Au, W. S. H., Kwok, W. W., Shum, A. K. Y., Wong, G. H. Y., & Lum, T. Y. S. (2021) · Journal of Medical Internet Research · doi:10.2196/26570 · Cited by 117
The mitigating role of blockchain-enabled supply chains during the COVID-19 pandemic
Xiong, Y., Lam, H. K. S., Kumar, A., Ngai, E. W. T., Xiu, C., & Wang, X. (2021) · International Journal of Operations & Production Management · doi:10.1108/ijopm-12-2020-0901 · Cited by 109
A Note on Increases in Inattentive Online Survey-Takers Since 2020
Ternovski, J., Orr, L., Kalla, J., & Aronow, P. M. (2022) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2022.002 · Cited by 103
Koch, T. K., Frischlich, L., & Lermer, E. (2023) · Journal of Applied Social Psychology · doi:10.1111/jasp.12959 · Cited by 123
Khattar, A., Jain, P. R., & Quadri, S. M. K. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9120955 · Cited by 102
Roozenbeek, J., Maertens, R., Herzog, S. M., Geers, M., Kurvers, R. H. J. M., Sultan, M., & van der Linden, S. (2022) · Judgment and Decision Making · doi:10.1017/s1930297500003570 · Cited by 106
Misbehaving in the Corona crisis: The role of anxiety and unfounded beliefs
Erceg, N., Ružojčić, M., & Galić, Z. (2020) · Current Psychology · doi:10.1007/s12144-020-01040-4 · Cited by 100
Covid-19 conspiracy beliefs and containment-related behaviour: The role of political trust
Karić, T., & Međedović, J. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2021.110697 · Cited by 78
Chowdhury, N. A., Khalid, A., & Turin, T. C. (2021) · Journal of Public Health · doi:10.1007/s10389-021-01565-3 · Cited by 111
Austin, E. W., Austin, B., Willoughby, J. F., Amram, O., & Domgaard, S. (2021) · Journal of Health Communication · doi:10.1080/10810730.2021.1899345 · Cited by 102
The COVID‐19 pandemic and the search for structure: Social media and conspiracy theories
Dow, B. J., Johnson, A. L., Wang, C. S., Whitson, J., & Menon, T. (2021) · Social and Personality Psychology Compass · doi:10.1111/spc3.12636 · Cited by 110
Metadata
- Authors
- Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G.
- Affiliations
- University of Regina (CA)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Pennycook, Gordon
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2020-06-30
- Volume
- 31
- Issue
- 7
- Pages
- 770-780
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- PMC
- PMC7366427
- OpenAlex
- W3013473577
- Cited by
- 1,715
- References
- 38
- Citations by year
- 2026: 111 · 2025: 188 · 2024: 238 · 2023: 299 · 2022: 329 · 2021: 416 · 2020: 132
- FWCI
- 393.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- miami foundation
- William and Flora Hewlett Foundation
- Omidyar Network
- John Templeton Foundation
- Canadian Institutes of Health Research
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- COVID-19 misinformation, social media sharing, truth discernment, fake news, cognitive reflection, scientific knowledge, misinformation prevention, sharing intention
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adolescent, Adult, Aged, Aged, 80 and over, Communication, Decision Making, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Coronavirus Infections, Information Dissemination, Young Adult, 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
