The rapid spread of misinformation online is of growing concern to communication researchers. Scientific misinformation can lead to ill-founded educational practices, health trends, and public policies. In an online survey-based experiment (N = 744), we corrected neuroscience myths via a mock Facebook newsfeed. We were able to reduce belief in the myths by presenting the subjects with corrective “related articles” immediately following the myth. We also found limited evidence that readers evaluate articles more positively when they are consistent with preexisting views. Our findings are consistent with previous research and extend research on corrective messaging strategies into a new context.
BessiA.ColettoM.DavidescuG. A.ScalaA.CaldarelliG.QuattrociocchiW. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PLoS One, 10(2), e0118093. doi:10.1371/journal.pone.0118093
2.
BettsK.MillerM.Tokuhama-EspinosaT.ShewokisP.AndersonA.BorjaC.. . . DekkerS. (2019). International report: Neuromyths and evidence-based practices in higher education. Newburyport, MA: Online Learning Consortium.
3.
BodeL.VragaE. K. (2015). In related news, that was wrong: The correction of misinformation through related stories functionality in social media. Journal of Communication, 65, 619-638. doi:10.1111/jcom.12166
4.
BodeL.VragaE. K. (2018). See something, say something: Correction of global health misinformation on social media. Health Communication, 33, 1131-1140. doi:10.1080/10410236.2017.13331312
5.
BrossardD.NisbetM. C. (2007). Deference to scientific authority among a low information public: Understanding U.S. opinion on agricultural biotechnology. International Journal of Public Opinion Research, 19, 24-52. doi:10.1093/ijpor/edl003
6.
BruerJ. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26(8), 4-16. doi:10.3102/0013189X026008004
7.
CacioppoJ. T.PettyR. E.KaoC. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48, 306-307. doi:10.1207/s15327752jpa4803_13
8.
ChanM. S.JonesC. R.Hall JamiesonK.AlbarracínD. (2017). Debunking: A meta-analysis of the psychological efficacy of messages countering misinformation. Psychological Science, 28, 1531-1546. doi:10.1177/0956797617714579
9.
DekkerS.LeeN. C.Howard-JonesP.JollesJ. (2012). Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology, 3, 429. doi:10.3389/fpsyg.2012.00429
10.
Del VicarioM.BessiA.ZolloF.PetroniF.ScalaA.CaldarelliG.. . . QuattrociocchiW. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113, 554-559. doi:10.1073/pnas.1517441113
11.
DixonG. N.ClarkeC. E. (2013). Heightening uncertainty around certain science: Media coverage, false balance, and the autism-vaccine controversy. Science Communication, 35, 358-382. doi:10.1177/1075547012458290
12.
DixonG.HmielowskiJ.MaY. (2017). Improving climate change acceptance among US conservatives through value-based message targeting. Science Communication, 39, 520-534. doi:10.1177/1075547017715473
13.
EckerU. K.LewandowskyS.TangD. T. (2010). Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & Cognition, 38, 1087-1100. doi:10.3758/MC.38.8.1087
14.
EnnalsR.BylerD.AgostaJ. M.RosarioB. (2010). What is disputed on the web? In Proceedings of the Fourth Workshop on Information Credibility (pp. 67-74). New York, NY: Association for Computing Machinery.
15.
FivesH.HuebnerW.BirnbaumA. S.NicolichM. (2014). Developing a measure of scientific literacy for middle school students. Science Education, 98, 549-580. doi:10.1002/sce.21115
16.
GardnerH. (1983). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books.
17.
GilbertD. T.TafarodiR. W.MaloneP. S. (1993). You can’t not believe everything you read. Journal of Personality and Social Psychology, 65, 221 doi:10.1037/0022-3514.65.2.221
18.
GreitemeyerT.FischerP.FreyD.Schulz-HardtS. (2009). Biased assimilation: The role of source position. European Journal of Social Psychology, 39, 22-39. doi:10.1002/ejsp.497
19.
GunningR. (1969). The Fog Index after twenty years. Journal of Business Communication, 6(2), 3-13. doi:10.1177/002194366900600202
20.
HoustonJ. B.HawthorneJ.PerreaultM. F.ParkE. H.Goldstein HodeM.HalliwellM. R.. . . GriffithS. A. (2015). Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1-22. doi:10.1111/disa.12092
21.
IllesJ.MoserM. A.McCormickJ. B.RacineE.BlakesleeS.CaplanA.. . . NicholsonC. (2010). Neurotalk: Improving the communication of neuroscience research. Nature Reviews Neuroscience, 11, 61-69. doi:10.1038/nrn2773
22.
JacksonS.O’KeefeD. J.JacobsS. (1988). The search for reliable generalizations about messages: A comparison of research strategies. Human Communication Research, 15, 127-142. doi:10.1111/j.1468-2958.1988.tb00174.x
23.
JeritJ.BarabasJ. (2012). Partisan perceptual bias and the information environment. Journal of Politics, 74, 672-684. doi:10.1017/S0022381612000187
24.
KahanD. M. (2017). “Ordinary science intelligence”: A science-comprehension measure for study of risk and science communication, with notes on evolution and climate change. Journal of Risk Research, 20, 995-1016. doi:10.1080/13669877.2016.1148067
25.
KimmerleJ.FlemmingD.FeinkohlI.CressU. (2015). How laypeople understand the tentativeness of medical research news in the media: An experimental study on the perception of information about deep brain stimulation. Science Communication, 37, 173-189. doi:10.1177/1075547014556541
26.
LaurieS. S. (1890). Lectures on language and linguistic method in school. Cambridge, England: Cambridge University Press.
27.
LewandowskyS.EckerU. K. H.SeifertC. M.SchwarzN.CookJ. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in Public Interest, 13, 106-131. doi:10.1177/1529100612451018
28.
LordC. G.RossL.LepperM. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098-2109. doi:10.1037/0022-3514.37.11.2098
29.
MacdonaldK.GermineL.AndersonA.ChristodoulouJ.McGrathL. M. (2017). Dispelling the myth: Training in education or neuroscience decreases but does not eliminate beliefs in neuromyths. Frontiers in Psychology, 8, 1314. doi:10.3389/fpsyg.2017.01314
30.
McinerneyC.BirdN.NucciM. (2004). The flow of scientific knowledge from lab to the lay public: The case of genetically modified food. Science Communication, 26, 44-74. doi:10.1177/1075547004267024
31.
National Science Board. (2016). Science and technology: Public attitudes and understanding. In Science and engineering indicators 2016 (pp. 7.1-7.94). Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/statistics/2016/nsb20161/uploads/1/nsb20161.pdf
32.
NyhanB.ReiflerJ. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32, 303-330. doi:10.1007/s11109-010-9112-2
33.
OlsonS. (Ed.). (2018). The science of science communication III: Inspiring novel collaborations and building capacity: Proceedings of a Colloquium. Washington, DC: National Academies Press.
PasquinelliE. (2012). Neuromyths: Why do they exist and persist?Mind, Brain, and Education, 6, 89-96. doi:10.1111/j.1751-228X.2012.01141.x
36.
PietschnigJ.VoracekM.FormannA. K. (2010). Mozart effect–Shmozart effect: A meta-analysis. Intelligence, 38, 314-323. doi:10.1016/j.intell.2010.03.001
37.
RadzikowskiJ.StefanidisA.JacobsenK. H.CroitoruA.CrooksA.DelamaterP. L. (2016). The measles vaccination narrative in Twitter: A quantitative analysis. JMIR Public Health and Surveillance, 2(1), e1. doi:10.2196/publichealth.5059
38.
SaerD. J. (1923). The effect of bilingualism on intelligence. British Journal of Psychology: General Section, 14, 25-38. doi:10.1111/j.2044-8295.1923.tb00110
39.
ScheufeleD. A.KrauseN. M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences, 116, 7662-7669. doi:10.1073/pnas.1805871115
40.
SmithT. W.DavernM.FreeseJ.HoutM. (2018). General social surveys, 1972-2016 [Machine-readable data file]. Chicago, IL: NORC at the University of Chicago.
41.
SouthwellB. G.ThorsonE. A. (2015). The prevalence, consequence, and remedy of misinformation in mass media systems. Journal of Communication, 65, 589-595. doi:10.1111/jcom.12168
42.
SteeleK. M.Dalla BellaS.PeretzI.DunlopT.DaweL. A.HumphreyG. K.. . . OlmsteadC. G. (1999). Prelude or requiem for the “Mozart effect?”Nature, 400, 827. doi:10.1038/23608
TakakuwaM. (2000). What’s wrong with the concept of cognitive development in studies of bilingualism?Bilingual Review, 25, 225-237.
45.
ThorsonE. (2016). Belief echoes: The persistent effects of corrected misinformation. Political Communication, 33, 460-480. doi:10.1080/10584609.2015.1102187
VosoughiS.RoyD.AralS. (2018). The spread of true and false news online. Science, 359, 1146-1151. doi:10.1126/science.aap9559
48.
VragaE. K.BodeL. (2017). Using expert sources to correct health misinformation in social media. Science Communication, 39, 621-645. doi:10.1177/1075547017731776
49.
WalterN.MurphyS. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85, 423-441. doi:10.1080/03637751.2018.1467564
50.
WalterN.TukachinskyR. (2019). A meta-analytic examination of the continued influence of misinformation in the face of correction: How powerful is it, why does it happen, and how to stop it?Communication Research. Advance online publication. doi:10.1177/0093650219854600
51.
WeisbergD. S.KeilF. C.GoodsteinJ.RawsonE.GrayJ. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20, 470-477. doi:10.1162/jocn.2008.20040
