This study aims to develop and test “myth-busting,” a promising strategy for science communicators to debunk common misconceptions among citizens. Widely spread counterfactual beliefs about biotechnologically produced food (BTF) as (a) unhealthy and unsafe, (b) unsustainable, and (c) unnatural served as context of application. A preregistered online experiment (N = 2,925) showed that myth-busting messages effectively correct misconceptions. Trust in science but not pre-existing beliefs moderated this effect. Reduced misconceptions in turn led to greater changes in attitudes toward BTF. The study exemplifies an interesting option for science communicators to address citizens’ misbeliefs about complex science and technology.
BeghinJ. C.GustafsonC. R. (2021). Consumer valuation of and attitudes towards novel foods produced with new plant engineering techniques: A review. Sustainability, 13(20), 1–17. https://doi.org/10.3390/su132011348
2.
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(4), 619–638. https://doi.org/10.1111/jcom.12166
3.
BodeL.VragaE. K.TullyM. (2021). Correcting misperceptions about genetically modified food on social media: Examining the impact of experts, social media heuristics, and the gateway belief model. Science Communication, 43(2), 225–251. https://doi.org/10.1177/1075547020981375
4.
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(1), 24–52. https://doi.org/10.1093/ijpor/edl003
5.
BucchiM.TrenchB. (2021). 1. Introduction: Science communication as the social con-versation around science. In TrenchB.BucchiM. (Eds.), Routledge handbook of public communication of science and technology (pp. 1–13). Routledge.
6.
CantleyM. (2012). European attitudes on the regulation of modern biotechnology and their consequences. GM Crops & Food, 3(1), 40–47. https://doi.org/10.4161/gmcr.19264
7.
ChanM.‑P.S., JonesC.AlbarracínD. (2017). Countering false beliefs: An analysis of the evidence and recommendations of best practices for the retraction and correction of scientific misinformation. In JamiesonK. H.KahanD. M.ScheufeleD. A. (Eds.), The Oxford handbook of the science of science communication (pp. 340–349). Oxford University Press.
8.
ChienY.‑H. (2014). Organ donation posters: Developing persuasive messages. Online Journal of Communication and Media Technologies, 4(4), 119–135. https://doi.org/10.29333/ojcmt/2490
9.
ClancyK. A.ClancyB. (2016). Growing monstrous organisms: The construction of anti-GMO visual rhetoric through digital media. Critical Studies in Media Communication, 33(3), 279–292. https://doi.org/10.1080/15295036.2016.1193670
10.
CookJ. (2019). Understanding and countering misinformation about climate change. In ChiluwaI.SamoilenkoS. A.ReynardL. J. (Eds.), Advances in media, entertainment, and the arts. Deception, fake news, and misinformation online (pp. 281–306). Information Science Reference. https://doi.org/10.4018/978-1-5225-8535-0.ch016
11.
CookJ.EckerU.LewandowskyS. (2015). Misinformation and how to correct it. In ScottR. A.KosslynS. M.BuchmannM. (Eds.), Emerging trends in the social and behavioral sciences (pp. 1–17). Wiley. https://doi.org/10.1002/9781118900772.etrds0222
12.
CookJ.LewandowskyS. (2016). Rational irrationality: Modeling climate change belief polarization using Bayesian networks. Topics in Cognitive Science, 8(1), 160–179. https://doi.org/10.1111/tops.12186
13.
Costa-FontM. (2011). Mapping social and environmental concerns and the acceptability of genetically modified organisms in the European Union. The Journal of Socio-Economics, 40(6), 903–908. https://doi.org/10.1016/j.socec.2011.08.014
14.
CummingsC. L.PetersD. (2023). Gene-edited foods and the public: The first representative survey study of the United States. Environmental Communication, 17(8), 965–974. https://doi.org/10.1080/17524032.2022.2086894
15.
DixonG. (2016). Applying the gateway belief model to genetically modified food perceptions: New insights and additional questions. Journal of Communication, 66(6), 888–908. https://doi.org/10.1111/jcom.12260
16.
EconomidisI.CichockaD.HögelJ. (Eds.). (2010). Project information. A decade of EU-funded GMO research: 2001-2010. Publications Office of the European Union. https://doi.org/10.2777/97784
17.
FeindtP. H.PoortvlietP. M. (2020). Consumer reactions to unfamiliar technologies: Mental and social formation of perceptions and attitudes toward nano and GM products. Journal of Risk Research, 23(4), 475–489. https://doi.org/10.1080/13669877.2019.1591487
18.
FlynnD. J.NyhanB.ReiflerJ. (2017). The nature and origins of misperceptions: Understanding false and unsupported beliefs about politics. Political Psychology, 38, 127–150. https://doi.org/10.1111/pops.12394
19.
FransenM. L.SmitE. G.VerleghP. W. J. (2015). Strategies and motives for resistance to persuasion: An integrative framework. Frontiers in Psychology, 6, Article 1201. https://doi.org/10.3389/fpsyg.2015.01201
20.
FrewerL. J.BergmannK.BrennanM.LionR.MeertensR.RoweG.SiegristM.VereijkenC. (2011). Consumer response to novel agri-food technologies: Implications for predicting consumer acceptance of emerging food technologies. Trends in Food Science & Technology, 22(8), 442–456. https://doi.org/10.1016/j.tifs.2011.05.005
21.
FrewerL. J.HowardC.HedderleyD.ShepherdR. (1999). Reactions to information about genetic engineering: Impact of source characteristics, perceived personal relevance, and persuasiveness. Public Understanding of Science, 8(1), 35–50. https://doi.org/10.1088/0963-6625/8/1/003
22.
FrewerL. J.HowardC.ShepherdR. (1996). The influence of realistic product exposure on attitudes towards genetic engineering of food. Food Quality and Preference, 7(1), 61–67. https://doi.org/10.1016/0950-3293(95)00017-8
23.
FrewerL. J.ScholdererJ.BredahlL. (2003). Communicating about the risks and benefits of genetically modified foods: The mediating role of trust. Risk Analysis: An Official Publication of the Society for Risk Analysis, 23(6), 1117–1133. https://doi.org/10.1111/j.0272-4332.2003.00385.x
24.
FrewerL. J.van der LansI. A.FischerA. R.ReindersM. J.MenozziD.ZhangX.van den BergI.ZimmermannK. L. (2013). Public perceptions of agri-food applications of genetic modification–a systematic review and meta-analysis. Trends in Food Science & Technology, 30(2), 142–152. https://doi.org/10.1016/j.tifs.2013.01.003
25.
FridkinK.KenneyP. J.WintersieckA. (2015). Liar, liar, pants on fire: How fact-checking influences citizens’ reactions to negative advertising. Political Communication, 32(1), 127–151. https://doi.org/10.1080/10584609.2014.914613
26.
FriedrichB.HackfortS.BoyerM.GottschlichD. (2019). Conflicts over GMOs and their contribution to food democracy. Politics and Governance, 7(4), 165–177. https://doi.org/10.17645/pag.v7i4.2082
27.
GarrettR. K.NisbetE. C.LynchE. K. (2013). Undermining the corrective effects of media-based political fact checking? The role of contextual cues and naïve theory. Journal of Communication, 63(4), 617–637. https://doi.org/10.1111/jcom.12038
28.
GarrettR. K.WeeksB. E. (2013). The promise and peril of real-time corrections to political misperceptions. In BruckmanA.CountsS.LampeC.TerveenL. (Eds.), Proceedings of the 2013 Conference on Computer Supported Cooperative Work (pp. 1047–1058). Association for Computing Machinery. https://doi.org/10.1145/2441776.2441895
29.
GaskellG.AllansdottirA.AllumN.CastroP.EsmerY.FischlerC.& WagerW. (2011). The 2010 Eurobarometer on the life sciences. Nature Biotechnology, 29(2), 113–114. https://doi.org/10.1038/nbt.1771
HameleersM.van der MeerT. G. L. A. (2019). Misinformation and polarization in a high-choice media environment: How effective are political fact-checkers?Communication Research, 47(2), 227–250. https://doi.org/10.1177/0093650218819671
32.
HanssenL.DijkstraA.SleenhoffS.FrewerL.GuttelingJ. M. (2018). Revisiting public debate on Genetic Modification and Genetically Modified Organisms. Explanations for contemporary Dutch public attitudes. Journal of Science Communication, 17(04), A01. https://doi.org/10.22323/2.17040201
33.
HeddyB. C.DanielsonR. W.SinatraG. M.GrahamJ. (2017). Modifying knowledge, emotions, and attitudes regarding genetically modified foods. The Journal of Experimental Education, 85(3), 513–533. https://doi.org/10.1080/00220973.2016.1260523
34.
HelliwellR.HartleyS.PearceW.O’NeillL. (2017). Why are NGOs sceptical of genome editing? NGOs’ opposition to agricultural biotechnologies is rooted in scepticism about the framing of problems and solutions, rather than just emotion and dogma. EMBO Reports, 18(12), 2090–2093. https://doi.org/10.15252/embr.201744385
35.
JansmaS. R.GosseltJ. F.KuipersK.de JongM. D. (2020). Technology legitimation in the public discourse: Applying the pillars of legitimacy on GM food. Technology Analysis & Strategic Management, 32(2), 195–207. https://doi.org/10.1080/09537325.2019.1648788
36.
JinS.LiW.NaabF. Z.ColesD.FrewerL. J. (2023). Consumer attitudes toward novel agrifood technologies: A critical review on genetic modification and synthetic biology. In KnowlesM. E.AnelichL. E.BoobisA. R.PoppingB. (Eds.), Present knowledge in food safety (pp. 1004–1014). Elsevier. https://doi.org/10.1016/B978-0-12-819470-6.00035-4
37.
JonesL. W.SinclairR. C.CourneyaK. S. (2003). The effects of source credibility and message framing on exercise intentions, behaviors, and attitudes: An integration of the elaboration likelihood model and prospect theory. Journal of Applied Social Psychology, 33(1), 179–196. https://doi.org/10.1111/j.1559-1816.2003.tb02078.x
KerrJ. R.WilsonM. S. (2018a). Changes in perceived scientific consensus shift beliefs about climate change and GM food safety. PLOS ONE, 13(7), Article e0200295. https://doi.org/10.1371/journal.pone.0200295
40.
KerrJ. R.WilsonM. S. (2018b). Perceptions of scientific consensus do not predict later beliefs about the reality of climate change: A test of the gateway belief model using cross-lagged panel analysis. Journal of Environmental Psychology, 59, 107–110. https://doi.org/10.1016/j.jenvp.2018.08.012
LandrumA. R.HallmanW. K.JamiesonK. H. (2019). Examining the impact of expert voices: Communicating the scientific consensus on genetically-modified organisms. Environmental Communication, 13(1), 51–70. https://doi.org/10.1080/17524032.2018.1502201
43.
LefebvreS.CookL. A.GriffithsM. A. (2019). Consumer perceptions of genetically modified foods: A mixed-method approach. Journal of Consumer Marketing, 36(1), 113–123. https://doi.org/10.1108/JCM-12-2016-2043
44.
LeonardM. J.KalinowskiS. T.AndrewsT. C. (2014). Misconceptions yesterday, today, and tomorrow. CBE Life Sciences Education, 13(2), 179–186. https://doi.org/10.1187/cbe.13-12-0244
LewandowskyS.EckerU. K. H.SeifertC. M.SchwarzN.CookJ. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest: A Journal of the American Psychological Society, 13(3), 106–131. https://doi.org/10.1177/1529100612451018
47.
LewandowskyS.GignacG. E.VaughanS. (2013). The pivotal role of perceived scientific consensus in acceptance of science. Nature Climate Change, 3(4), 399–404. https://doi.org/10.1038/nclimate1720
48.
LuchtJ. M. (2015). Public acceptance of plant biotechnology and GM crops. Viruses, 7(8), 4254–4281. https://doi.org/10.3390/v7082819
49.
LuskJ. L.McFaddenB. R.RickardB. J. (2015). Which biotech foods are most acceptable to the public?Biotechnology Journal, 10(1), 13–16. https://doi.org/10.1002/biot.201400561
50.
LuskJ. L.RoosenJ.BiebersteinA. (2014). Consumer acceptance of new food technologies: Causes and roots of controversies. Annual Review of Resource Economics, 6(1), 381–405. https://doi.org/10.1146/annurev-resource-100913-012735
McFaddenB. R. (2016). Examining the gap between science and public opinion about genetically modified food and global warming. PLOS ONE, 11(11), 1–14. https://doi.org/10.1371/journal.pone.0166140
53.
MichelF.SiegristM. (2019). How should importance of naturalness be measured? A comparison of different scales. Appetite, 140, 298–304. https://doi.org/10.1016/j.appet.2019.05.019
54.
NadelsonL.JorcykC.YangD.Jarratt SmithM.MatsonS.CornellK.HustingV. (2014). I just don’t trust them: The development and validation of an assessment instrument to measure trust in science and scientists. School Science and Mathematics, 114(2), 76–86. https://doi.org/10.1111/ssm.12051
55.
NicoliaA.ManzoA.VeronesiF.RoselliniD. (2014). An overview of the last 10 years of genetically engineered crop safety research. Critical Reviews in Biotechnology, 34(1), 77–88. https://doi.org/10.3109/07388551.2013.823595
56.
NyhanB. (2021). Why the backfire effect does not explain the durability of political misperceptions. Proceedings of the National Academy of Sciences of the United States of America, 118(15), 1–7. https://doi.org/10.1073/pnas.1912440117
57.
NyhanB.ReiflerJ. (2015). Does correcting myths about the flu vaccine work? An experimental evaluation of the effects of corrective information. Vaccine, 33(3), 459–464. https://doi.org/10.1016/j.vaccine.2014.11.017
PhamN.MandelN. (2019). What influences consumer evaluation of genetically modified foods?Journal of Public Policy & Marketing, 38(2), 263–279. https://doi.org/10.1177/0743915618818168
61.
PriestS. H.GoodwinJ. M.DahlstromM. F. (Eds.). (2018). Ethics and practice in science communication. The University of Chicago Press.
62.
RazaS. H.ZamanU.FerreiraP.FaríasP. (2021). An experimental evidence on public acceptance of genetically modified food through advertisement framing on health and environmental benefits, objective knowledge, and risk reduction. International Journal of Environmental Research and Public Health, 18(10), 5264. https://doi.org/10.3390/ijerph18105264
63.
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
RickardL. N.BritwumK.NobletC. L.EvansK. S. (2020). Factory-made or farm fresh? Measuring U.S. support for aquaculture as a food technology. Marine Policy, 115, 103858. https://doi.org/10.1016/j.marpol.2020.103858
66.
RománS.Sánchez-SilesL. M.SiegristM. (2017). The importance of food naturalness for consumers: Results of a systematic review. Trends in Food Science & Technology, 67, 44–57. https://doi.org/10.1016/j.tifs.2017.06.010
67.
RoseK. M.HowellE. L.SuL.Y.‑F.XenosM. A.BrossardD.ScheufeleD. A. (2019). Distinguishing scientific knowledge: The impact of different measures of knowledge on genetically modified food attitudes. Public Understanding of Science, 28(4), 449–467. https://doi.org/10.1177/0963662518824837
68.
RozinP.FischlerC.Shields-ArgelèsC. (2012). European and American perspectives on the meaning of natural. Appetite, 59(2), 448–455. https://doi.org/10.1016/j.appet.2012.06.001
69.
SchäferM. S. (2016). Mediated trust in science: Concept, measurement and perspectives for the “science of science communication.”Journal of Science Communication, 15(05), C02. https://doi.org/10.22323/2.15050302
70.
ScottS. E.InbarY.WirzC. D.BrossardD.RozinP. (2018). An overview of attitudes toward genetically engineered food. Annual Review of Nutrition, 38, 459–479. https://doi.org/10.1146/annurev-nutr-071715-051223
SinghA.KumarV.PoonamGuptaH. R. (2014). Genetically Modified Food: A Review on Mechanism of Production and Labeling Concern. Advances in Plants & Agriculture Research, 1(4), 121–127. https://doi.org/10.15406/apar.2014.01.00020
74.
SlebodaP.LagerkvistC.‑J. (2022). Tailored communication changes consumers’ attitudes and product preferences for genetically modified food. Food Quality and Preference, 96, 1–12. https://doi.org/10.1016/j.foodqual.2021.104419
75.
SpreenN.KlimmtC. (2024). Misconceptions still drive citizens’ scepticism towards biotechnologically produced food. Bulletin Of Science Technology & Society, 44(1–2), 33–43. https://doi.org/10.1177/02704676241258060
76.
ValenteM.ChavesC. (2018). Perceptions and valuation of GM food: A study on the impact and importance of information provision. Journal of Cleaner Production, 172, 4110–4118. https://doi.org/10.1016/j.jclepro.2017.02.042
77.
ValkenburgP. M.PeterJ. (2013). The differential susceptibility to media effects model. Journal of Communication, 63(2), 221–243. https://doi.org/10.1111/jcom.12024
78.
van der LindenS. L.LeiserowitzA. A.FeinbergG. D.MaibachE. W. (2015). The scientific consensus on climate change as a gateway belief: Experimental evidence. PLOS ONE, 10(2), Article e0118489. https://doi.org/10.1371/journal.pone.0118489
79.
van der LindenS. L.LeiserowitzA. A.MaibachE. (2017). Gateway illusion or cultural cognition confusion?Journal of Science Communication, 16(05), A04. https://doi.org/10.22323/2.16050204
80.
van der LindenS. L.LeiserowitzA. A.MaibachE. (2019). The gateway belief model: A large-scale replication. Journal of Environmental Psychology, 62, 49–58. https://doi.org/10.1016/j.jenvp.2019.01.009
81.
VandermoereF.BlanchemancheS.BiebersteinA.MaretteS.RoosenJ. (2010). The morality of attitudes toward nanotechnology: About God, techno-scientific progress, and interfering with nature. Journal of Nanoparticle Research: An Interdisciplinary Forum for Nanoscale Science and Technology, 12(2), 373–381. https://doi.org/10.1007/s11051-009-9809-5
82.
van StekelenburgA.SchaapG.VelingH.BuijzenM. (2021). Boosting understanding and identification of scientific consensus can help to correct false beliefs. https://doi.org/10.31219/osf.io/bdvyr
83.
Vega RodríguezA.Rodríguez-OramasC.Sanjuán VelázquezE.La Hardisson de TorreA.Rubio ArmendárizC.Carrascosa IruzubietaC. (2022). Myths and realities about genetically modified food: A risk-benefit analysis. Applied Sciences, 12(6), 1–24. https://doi.org/10.3390/app12062861
84.
VragaE. K.BodeL. (2017). Using expert sources to correct health misinformation in social media. Science Communication, 39(5), 621–645. https://doi.org/10.1177/1075547017731776
85.
VragaE. K.BodeL.TullyM. (2022). Creating news literacy messages to enhance expert corrections of misinformation on Twitter. Communication Research, 49(2), 245–267. https://doi.org/10.1177/0093650219898094
86.
WalterN.CohenJ.HolbertR. L.MoragY. (2020). Fact-checking: A meta-analysis of what works and for whom. Political Communication, 37(3), 350–375. https://doi.org/10.1080/10584609.2019.1668894
87.
WalterN.MurphyS. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85(3), 423–441. https://doi.org/10.1080/03637751.2018.1467564
88.
WangY. (2021). Debunking misinformation about genetically modified food safety on social media: Can Heuristic cues mitigate biased assimilation?Science Communication, 43(4), 460–485. https://doi.org/10.1177/10755470211022024
89.
YuanS.MaW.BesleyJ. C. (2019). Should scientists talk about GMOs nicely? Exploring the effects of communication styles, source expertise, and preexisting attitude. Science Communication, 41(3), 267–290. https://doi.org/10.1177/1075547019837623
90.
ZhuX.XieX. (2015). Effects of knowledge on attitude formation and change toward genetically modified foods. Risk Analysis: An Official Publication of the Society for Risk Analysis, 35(5), 790–810. https://doi.org/10.1111/risa.12319
