Sage Journals: Discover world-class research

Abstract

This study expands on existing research about correcting misinformation on social media. Using an experimental design, we explore the effects of three truth signals related to stories shared on social media: whether the person posting the story says it is true, whether the replies to the story say it is true, or whether the story itself is actually true. Our results suggest that individuals should not share misinformation in order to debunk it, as audiences assume sharing is an endorsement. Additionally, while two responses debunking the post do reduce belief in the post’s veracity and argument, this process occurs equally when the story is false (thereby reducing misperceptions) as when it is true (thus reinforcing misperceptions). Our results have implications for individuals interested in correcting health misinformation on social media and for the organizations that support their efforts.

Keywords

misinformation correction health communication debunking

Get full access to this article

View all access options for this article.

References

Allcott

Gentzkow

(2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236.

Amazeen

M. A.

Thorson

Muddiman

Graves

(2018). Correcting political and consumer misperceptions: The effectiveness and effects of rating scale versus contextual correction formats. Journalism & Mass Communication Quarterly, 95(1), 28–48.

Avaaz. (2020). Facebook’s algorithm: A major threat to public health. https://secure.avaaz.org/campaign/en/facebook_threat_health/

Bigsby

Hovick

S. R.

(2018). Understanding associations between information seeking and scanning and health risk behaviors: An early test of the structural influence model. Health Communication, 33(3), 315-325.

Bode

Vraga

E. K.

(2015). In related news, that was wrong: The correction of misinformation through related stories functionality in social media. Journal of Communication, 65(4), 619–638. https://doi.org/10.1111/jcom.12166

Bode

Vraga

E. K.

(2018). See something, say something: Correction of global health misinformation on social media. Health Communication, 33(9), 1131–1140.

Chong

Druckman

J. N.

(2007). Framing theory. Annual Review of Political Science, 10, 103–126.

Darcy

(2021). Headlines lacking context exploited by anti-vaccine activists to wrongly suggest danger, study finds. CNN.com . https://www.cnn.com/2021/03/18/media/covid-19-vaccine-headlines/index.html

Druckman

J. N.

(2004). Political preference formation: Competition, deliberation, and the (ir) relevance of framing effects. American Political Science Review, 98(4), 671–686.

10.

Ecker

U. K.

Lewandowsky

Swire

Chang

(2011). Correcting false information in memory: Manipulating the strength of misinformation encoding and its retraction. Psychonomic Bulletin & Review, 18(3), 570–578.

11.

Gabielkov

Ramachandran

Chaintreau

Legout

(2016). Social clicks: What and who gets read on Twitter? [Conference session]. Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science— SIGMETRICS’16 (pp. 179–192). New York, NY: Association for Computing Machinery.

12.

Gyenes

(2020). A standard of care for health misinformation: Lessons from Global Fact. https://meedan.com/blog/a-standard-of-care-for-health-misinformation-lessons-from-global-fact/

13.

Harris

(2019). Can maternal fluoride consumption during pregnancy lower children’s intelligence? NPR. https://www.npr.org/sections/health-shots/2019/08/19/752376080/can-maternal-fluoride-consumption-during-pregnancy-lower-childrens-intelligence

14.

Jolley

Douglas

K. M.

(2014). The effects of anti-vaccine conspiracy theories on vaccination intentions. PLoS One, 9(2), e89177.

15.

Kang

Bae

Zhang

Sundar

S. S.

(2011). Source cues in online news: Is the proximate source more powerful than distal sources? Journalism & Mass Communication Quarterly, 88(4), 719–736.

16.

Keller

P. A.

Lehmann

D. R.

(2008). Designing effective health communications: A meta-analysis. Journal of Public Policy & Marketing, 27(2), 117–130.

17.

Kim

J. N.

Gil de Zúñiga

(2021). Pseudo-information, media, publics, and the failing marketplace of ideas: Theory. American Behavioral Scientist, 65(2), 163–179.

18.

Knight/Gallup. (2018). Americans’ views of misinformation and how to counteract it. Knight Foundation. https://knightfoundation.org/reports/americans-views-of-misinformation-in-the-news-and-how-to-counteract-it/

19.

Krishna

Thompson

T. L.

(2021). Misinformation about health: A review of health communication and misinformation scholarship. American Behavioral Scientist, 65(2), 316–332.

20.

Lee

Shin

S. Y.

(2021). Mediated misinformation: Questions answered, more questions to ask. American Behavioral Scientist, 65(2), 259–276.

21.

Lewandowsky

Cook

Ecker

U. K. H.

Albarracín

Amazeen

M. A.

Kendeou

Lombardi

Newman

E. J.

Pennycook

Porter

Rand

D. G.

Rapp

D. N.

Reifler

Roozenbeek

Schmid

Seifert

C. M.

Sinatra

G. M.

Swire-Thompson

van der Linden

. . . Zaragoza

M. S.

(2020). The debunking handbook 2020. https://sks.to/db2020

22.

Lewandowsky

Ecker

U. K. H.

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.

23.

Margolin

D. B.

Hannak

Weber

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

24.

Metaxas

P. T.

Mustafaraj

Wong

Zeng

O’Keefe

Finn

(2015). What Do retweets indicate? Results from user survey and meta-review of research [Conference session]. Proceedings of the International AAAI Conference on Web and Social Media (pp. 658–661).

25.

Mitchell

Gottfried

Stocking

Walker

Fedeli

(2019). Many Americans say made up news is a critical problem that needs to be fixed. Pew Research Center. https://www.journalism.org/2019/06/05/many-americans-say-made-up-news-is-a-critical-problem-that-needs-to-be-fixed/

26.

Molina

M. D.

Sundar

S. S.

Lee

(2021). “Fake news” is not simply false information: A concept explication and taxonomy of online content. American Behavioral Scientist, 65(2), 180–212.

27.

Molyneux

Mourão

R. R.

(2019). Political journalists’ normalization of Twitter: Interaction and new affordances. Journalism Studies, 20(2), 248–266.

28.

Oeldorf-Hirsch

DeVoss

C. L.

(2020). Who posted that story? Processing layered sources in Facebook news posts. Journalism & Mass Communication Quarterly, 97(1), 141–160.

29.

Y. W.

Park

C. H.

(2021). Machine cleaning of online opinion spam: Developing a machine-learning algorithm for detecting deceptive comments. American Behavioral Scientist, 65(2), 389–403.

30.

Pew. (2019). Social media fact sheet. Pew Research Center. https://www.pewresearch.org/internet/fact-sheet/social-media/

31.

Porter

Wood

T. J.

(2019). False alarm: The truth about political mistruths in the Trump era. Cambridge University Press.

32.

Smith

C. N.

Seitz

H. H.

(2019). Correcting misinformation about neuroscience via social media. Science Communication, 41(6), 790–819.

33.

Stroud

N. J.

Jennings

Collier

Tenorio

A. C.

Sterlin

Xia

M. F.

(2020, May). The effectiveness of fact check headlines on social media: Field Experiments across four continents [Paper presentation]. Paper presented to the International Communication Association, Gold Coast, Australia.

34.

Sülflow

Schäfer

Winter

(2019). Selective attention in the news feed: An eye-tracking study on the perception and selection of political news posts on Facebook. New Media & Society, 21(1), 168–190.

35.

Tandoc

E. C.

Jr. Lim

Ling

(2020). Diffusion of disinformation: How social media users respond to fake news and why. Journalism, 21(3), 381–398.

36.

Tandoc

E. C.

Jr. Lim

Z. W.

Ling

(2018). Defining “fake news”: A typology of scholarly definitions. Digital Journalism, 6(2), 137–153.

37.

Tsfati

Boomgaarden

H. G.

Strömbäck

Vliegenthart

Damstra

Lindgren

(2020). Causes and consequences of mainstream media dissemination of fake news: Literature review and synthesis. Annals of the International Communication Association, 44(2), 157–173.

38.

Turcotte

York

Irving

Scholl

R. M.

Pingree

R. J.

(2015). News recommendations from social media opinion leaders: Effects on media trust and information seeking. Journal of Computer-Mediated Communication, 20(5), 520–535.

39.

van der Meer

T. G.

Jin

. (2020). Seeking formula for misinformation treatment in public health crises: The effects of corrective information type and source. Health Communication, 35(5), 560–575.

40.

Vraga

Bode

Troller-Renfree

(2016). Beyond self-reports: Using eye tracking to measure topic and style differences in attention to social media content. Communication Methods and Measures, 10(2–3), 149–164.

41.

Vraga

E. K.

Bode

(2017). Using expert sources to correct health misinformation in social media. Science Communication, 39(5), 621–645.

42.

Vraga

E. K.

Bode

(2020). Correction as a solution for health misinformation on social media. American Journal of Public Health, 110(S3), S278–S280.

43.

Vraga

E. K.

Kim

S. C.

Cook

Bode

(2020). Testing the effectiveness of correction placement and type on Instagram. The International Journal of Press/Politics, 25(4), 632–652.

44.

Walter

Brooks

J. J.

Saucier

C. J.

Suresh

(2021). Evaluating the impact of attempts to correct health misinformation on social media: A meta-analysis. Health Communication, 36(13), 1776–1784.

45.

Walter

Murphy

S. T.

(2018). How to unring the bell: A meta-analytic appraoch to correction of misinformation. Communication Monographs, 85(3), 423–441.

46.

Wardle

(2017). Fake news. It’s complicated. Medium.com . https://medium.com/1st-draft/fake-news-its-complicated-d0f773766c79

47.

Wilner

Holton

(2020). Breast cancer prevention and treatment: Misinformation on pinterest, 2018. American Journal of Public Health, 110(S3), S300–S304.

48.

Zaller

J. R.

(1992). The nature and origins of mass opinion. Cambridge University Press.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

1.36 MB

Correcting What’s True: Testing Competing Claims About Health Misinformation on Social Media

Abstract

Keywords

Get full access to this article

References

Supplementary Material