Sage Journals: Discover world-class research

Abstract

Misinformation poses a significant societal challenge, yet research lacks a standardized framework for conceptualizing and operationalizing correction behaviors. This study identifies four types: verification, rejection, public, and private correction engagement. A 14-item scale was developed in Study 1 using 23 focus groups (N = 120) and expert review (N = 12). Study 2 (N = 1,063) and Study 3 (N = 467) were conducted in China using Mandarin to validate the scale’s reliability and construct validity through confirmatory factor analyses. The scale offers a robust tool for studying correction behaviors and provides theoretical and practical insights for misinformation research.

Keywords

misinformation correction scale development conceptualization operationalization

Get full access to this article

View all access options for this article.

References

Benegal

S. D.

Scruggs

D. A.

(2018). Correcting misinformation about climate change: The impact of partisanship in an experimental setting. Climatic Change, 148(1–2), 61–80. https://doi.org/10.1007/s10584-018-2192-4

Chan

(2024). Verification behaviors and countermeasures in the age of misinformation. Journalism & Mass Communication Quarterly, 101(1), 13–19. https://doi.org/10.1177/10776990231223998

Chen

(2022). Let’s fight the infodemic: The third-person effect process of misinformation during public health emergencies. Internet Research, 32(4), 1357–1377. https://doi.org/10.1108/INTR-03-2021-0194

Chen

Tang

(2023a). Examining the persuasion process of narrative fear appeals on health misinformation correction. Information, Communication & Society, 26(15), 2923–2941. https://doi.org/10.1080/1369118X.2022.2128849

Chen

Tang

(2023b). Intention of health experts to counter health misinformation in social media: Effects of perceived threat to online users, correction efficacy, and self-affirmation. Public Understanding of Science, 32(3), 284–303. https://doi.org/10.1177/09636625221138357

Chen

Wang

Peng

T. Q.

(2018). Nature and diffusion of gynecologic cancer related misinformation on social media: Analysis of tweets. Journal of Medical Internet Research, 20(10), e11515. https://doi.org/10.2196/11515

Chen

Zheng

(2023). Examining factors influencing public knowledge, risk perception, and policy support for waste classification: A multigroup comparison of the cognitive mediation model based on gender differences. Environmental Communication, 17(7), 759–774. https://doi.org/10.1080/17524032.2023.2253499

Chinese Academy of Social Sciences. (2023). Annual report on national internet user behavior. Social Sciences Academic Press.

Cinelli

Morales

G. D.

Galeazzi

Quattrociocchi

Starnini

(2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118

10.

Churchill

G. A

Jr . (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110

11.

Costello

A. B.

Osborne

(2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1). https://doi.org/10.7275/jyj1-4868

12.

de Winter

J. C.

Dodou

D. I. M. I. T. R. A.

Wieringa

P. A

. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44(2), 147–181. https://doi.org/10.1080/00273170902794206

13.

Del Vicario

Bessi

Zollo

Petroni

Scala

Caldarelli

Quattrociocchi

. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554–559. https://doi.org/10.1073/pnas.1517441113

14.

Dinev

Goo

Nam

(2009). User behaviour towards protective information technologies: the role of national cultural differences. Information Systems Journal, 19(4), 391–412. https://doi.org/10.1111/j.1365-2575.2007.00289.x

15.

Ecker

U. K.

Ang

L. C.

(2019). Political attitudes and the processing of misinformation corrections. Political Psychology, 40(2), 241–260. https://doi.org/10.1111/pops.12494

16.

Ecker

U. K.

Lewandowsky

Cook

Schmid

Fazio

L. K.

Brashier

...Amazeen

M. A.

(2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y

17.

Ecker

U. K.

Prike

Paver

A. B.

Scott

R. J.

Swire-Thompson

(2024). Don’t believe them! Reducing misinformation influence through source discreditation. Cognitive Research: Principles and Implications, 9(1), 52. https://doi.org/10.1186/s41235-024-00581-7

18.

Fallis

(2015). What is disinformation? Library Trends, 63(3), 401–426. https://muse.jhu.edu/article/579342

19.

Finstad

(2010). The usability metric for user experience. Interacting with Computers, 22(5), 323–327. https://doi.org/10.1016/j.intcom.2010.04.004

20.

Finch

T. L.

Bamford

Deary

Sabin

Parry

S. W.

(2014). Making sense of a cognitive behavioural therapy intervention for fear of falling: Qualitative study of intervention development. BMC Health Services Research, 14, 1–12. https://doi.org/10.1186/1472-6963-14-436

21.

Floyd

F. J.

Widaman

K. F.

(1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286. https://doi.org/10.1037/1040-3590.7.3.286

22.

Flynn

D. J.

Nyhan

Reifler

(2017). The nature and origins of misperceptions: Understanding false and unsupported beliefs about politics. Political Psychology, 38(S1), 127–150. https://doi.org/10.1111/pops.12394

23.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

24.

Furr

R. M.

(2011). Scale Construction and Psychometrics for Social and Personality Psychology. SAGE Publications.

25.

Garrett

R. K.

Poulsen

(2019). Flagging Facebook falsehoods: Self-identified humor warnings outperform fact checker and peer warnings. Journal of Computer-Mediated Communication, 24(5), 240–258. https://doi.org/10.1093/jcmc/zmz012

26.

Guess

A. M.

Coppock

(2020). Does counter-attitudinal information cause backlash? Results from three large survey experiments. British Journal of Political Science, 50(4), 1497–1515. https://doi.org/10.1017/S0007123418000327

27.

Guess

A. M.

Nagler

Tucker

J. A.

(2020). Exposure to untrustworthy websites in the 2016 US election. Nature Human Behaviour, 4(5), 472–480. https://doi.org/10.1038/s41562-020-0833-x

28.

Han

(2015). Manufacturing consent in cyberspace: China’s “fifty-cent army.” Journal of Current Chinese Affairs, 44(2), 105–134. https://doi.org/10.1177/186810261504400205

29.

Haynes

S. N.

Richard

D. C. S.

Kubany

E. S.

(1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7, 238–247. https://doi.org/10.1037/1040-3590.7.3.238

30.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

31.

Hinkin

T. R.

(1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121. https://doi.org/10.1177/109442819800100106

32.

Houston

J. B.

Spialek

M. L.

(2019). The development and initial validation of the citizen disaster communication assessment. Communication Research, 46(1), 29–55. https://doi.org/10.1177/0093650217697521

33.

Hsieh

H. F.

Shannon

S. E.

(2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687

34.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

35.

Joshi

Kale

Chandel

Pal

D. K.

(2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396. https://doi.org/10.9734/BJAST/2015/14975

36.

Kemp

P. L.

Goldman

A. C.

Wahlheim

C. N.

(2024). On the role of memory in misinformation corrections: Repeated exposure, correction durability, and source credibility. Current Opinion in Psychology, 56, 101783. https://doi.org/10.1016/j.copsyc.2024.101783

37.

Kim

J. N.

Grunig

J. E.

(2011). Problem solving and communicative action: A situational theory of problem solving. Journal of Communication, 61(1), 120–149. https://doi.org/10.1111/j.1460-2466.2010.01529.x

38.

Krueger

R. A.

Casey

M. A.

(2014). Focus groups: A practical guide for applied research (5th ed.). Sage.

39.

Kumar

Shah

(2018). False information on web and social media: A survey. arXiv. https://arxiv.org/abs/1804.08559

40.

Lazer

D. M.

Baum

M. A.

Benkler

Berinsky

A. J.

Greenhill

K. M.

Menczer

Zittrain

J. L.

(2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

41.

Lewandowsky

Ecker

U. K.

Seifert

C. M.

Schwarz

Cook

(2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131. https://doi.org/10.1177/1529100612451018

42.

Lim

J. S.

(2017). The third-person effect of online advertising of cosmetic surgery: A path model for predicting restrictive versus corrective actions. Journalism & Mass Communication Quarterly, 94(4), 972–993. https://doi.org/10.1177/1077699016687722

43.

Luo

Zhu

Chen

(2024). What motivates people to counter misinformation on social media? Unpacking the roles of perceived consequences, third-person perception, and social media use. Online Information Review, 48(1), 105–122. https://doi.org/10.1108/OIR-09-2022-0507

44.

Lynn

M. R.

(1986). Determination and quantification of content validity. Nursing Research, 35, 382–386. https://doi.org/10.1097/00006199-198611000-00017

45.

Margolin

D. B.

Hannak

Weber

(2018). Political fact-checking on Twitter: When do corrections have an effect? Political Communication, 35(2), 196–219. https://doi.org/10.1080/10584609.2017.1334018

46.

McKeever

B. W.

McKeever

Choi

Huang

(2023). From advocacy to activism: A multi-dimensional scale of communicative, collective, and combative behaviors. Journalism & Mass Communication Quarterly, 100(3), 569–594. https://doi.org/10.1177/10776990231161035

47.

McQuail

(2010). McQuail’s mass communication theory. Sage.

48.

Molina

M. D.

Sundar

S. S.

Lee

(2021). “Fake news” is not simply false information: A concept explication and taxonomy of online content. Social Media + Society, 7(1), 180–212. https://doi.org/10.1177/0002764219878224

49.

Morgan

D. L.

Krueger

R. A.

King

J. A.

(1998). The focus group guidebook. Sage.

50.

Muthén

(2017). Mplus. In van der Linden

W. J.

(Ed.), Handbook of item response theory (pp. 1–12). Chapman and Hall/CRC. https://doi.org/10.1201/9781315117430

51.

Napoli

(2019). Social media and the public interest: Media regulation in the disinformation age. Columbia University Press.

52.

Newman

Fletcher

Eddy

Robertson

C. T.

Kleis Nielsen

(2023). Reuters Institute Digital News Report 2023. Reuters University, University of Oxford. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2023

53.

Ognyanova

(2024). Fact-checking: Journalistic strategies and audience outcomes in diverse national contexts. Journalism & Mass Communication Quarterly, 101(2), 313–319. https://doi.org/10.1177/10776990241238247

54.

Lee

S. Y.

(2019). When do people verify and share health rumors on social media? The effects of message importance, health literacy, and health consciousness. Health Communication, 34(9), 1055–1064. https://doi.org/10.1080/10810730.2019.1677824

55.

Pennycook

Rand

D. G.

(2018). The implied truth effect: Attaching warnings to a subset of fake news stories increases perceived accuracy of stories without warnings. Management Science, 66(11), 4944–4957. https://doi.org/10.1287/mnsc.2019.3478

56.

Pennycook

Rand

D. G.

(2019). Fighting misinformation on social media using crowdsourced judgments of news source quality. Proceedings of the National Academy of Sciences, 116(7), 2521–2526. https://doi.org/10.1073/pnas.1806781116

57.

Pew Research Center. (2020). 64% of Americans say social media have a mostly negative effect on the way things are going in the U.S. today. https://www.pewresearch.org/short-reads/2020/10/15/64-of-americans-say-social-media-have-a-mostly-negative-effect-on-the-way-things-are-going-in-the-u-s-today/

58.

Pew Research Center. (2025). Widespread global public concern about made-up news. https://www.pewresearch.org/global/2025/04/24/widespread-global-public-concern-about-made-up-news/

59.

Porter

Wood

T. J.

(2024). Factual corrections: Concerns and current evidence. Current Opinion in Psychology, 55, 101715. https://doi.org/10.1016/j.copsyc.2023.101715

60.

Repnikova

(2022). Rethinking China’s Soft Power:“Pragmatic Enticement” of Confucius Institutes in Ethiopia. The China Quarterly, 250, 440–463.

61.

Savolainen

(2008). Everyday information practices: A social phenomenological perspective. Scarecrow Press.

62.

Sun

Pan

(2025). Self-correction or other-correction: The effects of source consistency and ways of correction on sharing intention of health misinformation correction. Health Communication, 40(3), 361–371. https://doi.org/10.1080/10410236.2024.2346674

63.

Swire

Ecker

(2018). Misinformation and its correction: Cognitive mechanisms and recommendations for mass communication.In Southwell

B. G.

Thorson

E. A.

Sheble

(Eds.), Misinformation and mass audiences (pp. 195–211). University of Texas Press.

64.

Tandoc

E. C.

Jr. Lim

Z. W.

Ling

(2018). Defining “fake news”: A typology of scholarly definitions. Digital Journalism, 6(2), 137–153. https://doi.org/10.1080/21670811.2017.1360143

65.

Tucker

J. A.

Guess

Barberá

Vaccari

Siegel

Sanovich

Stukal

Nyhan

(2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Hewlett Foundation. https://doi.org/10.2139/ssrn.3144139

66.

van der Linden

Roozenbeek

Compton

. (2020). Inoculating against fake news about COVID-19. Frontiers in Psychology, 11, 566790. https://doi.org/10.3389/fpsyg.2020.566790

67.

Vijaykumar

Rogerson

D. T.

Jin

Oliveira-Costa

M. S.

(2022). Dynamics of social corrections to peers sharing COVID-19 misinformation on WhatsApp in Brazil. Journal of the American Medical Informatics Association, 29(1), 33–42. https://doi.org/10.1093/jamia/ocab219

68.

Voorhees

C. M.

Brady

M. K.

Calantone

Ramirez

(2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44, 119–134. https://doi.org/10.1007/s11747-015-0455-4

69.

Vraga

E. K.

Bode

(2017). Using expert sources to correct health misinformation in social media. Science Communication, 39(5), 621–645. https://doi.org/10.1177/1075547017731776

70.

Vraga

E. K.

Bode

(2018). I do not believe you: How providing a source corrects health misperceptions across social media platforms. Information, Communication & Society, 21(10), 1337–1353. https://doi.org/10.1080/1369118X.2017.1313883

71.

Vraga

E. K.

Bode

(2021). Addressing COVID-19 misinformation on social media preemptively and responsively. Emerging Infectious Diseases, 27(2), 396–403. https://doi.org/10.3201/eid2702.203139

72.

Wang

S. A.

Pang

M. S.

Pavlou

P. A.

(2022). Seeing is believing? How including a video in fake news influences users’ reporting of the fake news to social media platforms. MIS Quarterly, 45(3), 1323–1354. https://doi.org/10.25300/MISQ/2022/16296

73.

Wardle

Derakhshan

(2017). Information disorder: Toward an interdisciplinary framework for research and policymaking. Council of Europe Report. https://rm.coe.int/information-disorder-toward-an-interdisciplinary-framework-for-researc/168076277c

74.

Wei

V. H.

Zhang

Qiu

J. L.

(2024). Examining the impact of digital information environments, information processing, and presumed influence on behavioral responses to COVID-19 misinformation in Asia. New Media & Society, 27(9), 5257–5280. https://doi.org/10.1177/14614448241252391

75.

Wheaton

Muthen

Alwin

D. F.

Summers

G. F.

(1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84–136. https://doi.org/10.2307/270754

76.

White

C. M.

Cutello

C. A.

Gummerum

Hanoch

(2018). A cross-cultural study of risky online self-presentation. Cyberpsychology, Behavior, and Social Networking, 21(1), 25–31. https://doi.org/10.1089/cyber.2016.0660

77.

Wood

Porter

(2019). The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior, 41, 135–163. https://doi.org/10.1007/s11109-018-9443-y

78.

World Economic Forum. (2013). Global risks report 2013 (8th ed.). http://www3.weforum.org/docs/WEF_GlobalRisks_Report_2013.pdf

79.

Xiao

(2022). Not doomed: Examining the path from misinformation exposure to verification and correction in the context of COVID-19 pandemic. Telematics and Informatics, 74, 101890. https://doi.org/10.1016/j.tele.2022.101890

80.

York

Ponder

J. D.

Humphries

Goodall

Beam

Winters

(2020). Effects of fact-checking political misinformation on perceptual accuracy and epistemic political efficacy. Journalism & Mass Communication Quarterly, 97(4), 958–980. https://doi.org/10.1177/1077699019890119

81.

Zannettou

Sirivianos

Blackburn

Kourtellis

(2018). The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. Journal of Data and Information Quality, 11(3), Article 10. https://doi.org/10.1145/3309699

82.

Zeng

H. K.

S. Y.

S. C. S.

(2024). Credibility of misinformation source moderates the effectiveness of corrective messages on social media. Public Understanding of Science, 33(5), 587–603. https://doi.org/10.1177/09636625231215979

83.

Zollo

Quattrociocchi

(2018). Misinformation spreading on Facebook. Social Media + Society, 4(3), 177–196. https://doi.org/10.1007/978-3-319-77332-2_10

Developing and Validating a Four-Dimensional Misinformation Correction Scale: A Mixed-Methods Approach

Abstract

Keywords

Get full access to this article

References