Restricted accessResearch articleFirst published online 2026-3
Social Media Misinformation Wars: How Message Features,Political Cynicism,and Conspiracy Beliefs Shape Government-Led Public Health Debunking Effectiveness
This study investigates the effectiveness of public health institutions’ misinformation debunking on social media by examining the impact of message features—social media intermediaries, message framing, and social cues—alongside the moderating roles of political cynicism and conspiracy beliefs. We conducted preregistered survey experiments in Hong Kong, the Netherlands, and the United States (total N = 2,769). Results show that sponsored messages outperformed AI recommendations. Causal framing would backfire for the cynics (in both Hong Kong and the Netherlands). In the United States, peer-shared messages enhanced source and message evaluations among those with higher conspiracy beliefs.
AnspachN. M. (2017). The new personal influence: How our Facebook friends influence the news we read. Political communication, 34(4), 590–606. https://doi.org/10.1080/10584609.2017.1316329
2.
AppelmanA.SundarS. S. (2016). Measuring message credibility: Construction and validation of an exclusive scale. Journalism & Mass Communication Quarterly, 93(1), 59–79. https://doi.org/10.1177/10776990156060
3.
BalintA. (2023). “It’s in the air”—Sponsored editorial content as a path for stealth government propaganda: The case of Israeli Media. In J. Hardy (Ed.), Sponsored editorial content in digital journalism (pp. 88–109). Routledge.
4.
BansalG.ZahediF. M. (2014). Trust-discount tradeoff in three contexts: Frugality moderating privacy and security concerns. Journal of Computer Information Systems, 55(1), 13–29. https://doi.org/10.1080/08874417.2014.11645737
5.
BarthelS.ParkerJ.ErnstsonH. (2015). Food and green space in cities: A resilience lens on gardens and urban environmental movements. Urban Studies, 52(7), 1321–1338. https://doi.org/10.1177/0042098012472744
6.
BennettW. L.LivingstonS. (2018). The disinformation order: Disruptive communication and the decline of democratic institutions. European Journal of Communication, 33(2), 122–139. https://doi.org/10.1177/0267323118760317
7.
BodeL.VragaE. K. (2018). See something, say something: Correction of global health misinformation on social media. Health Communication, 33(9), 1131–1140. https://doi.org/10.1080/10410236.2017.1331312
8.
BondR. M.FarissC. J.JonesJ. J.KramerA. D.MarlowC.SettleJ. E.FowlerJ. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. https://doi.org/10.1038/nature11421
9.
BradfordA. (2023). Digital empires: The global battle to regulate technology. Oxford University Press.
10.
BradshawS.HowardP. (2017). Troops, trolls and troublemakers: A global inventory of organized social media manipulation. Computational Propaganda Research Project.
11.
BruderM.HaffkeP.NeaveN.NouripanahN.ImhoffR. (2013). Measuring individual differences in generic beliefs in conspiracy theories across cultures: Conspiracy Mentality Questionnaire. Frontiers in Psychology, 4, 225. https://doi.org/10.3389/fpsyg.2013.00225
12.
BrunsH.DessartF. J.KrawczykM.LewandowskyS.PantaziM.PennycookG.SchmidP.SmillieL. (2024). Investigating the role of source and source trust in prebunks and debunks of misinformation in online experiments across four EU countries. Scientific Reports, 14(1), 20723. https://www.nature.com/articles/s41598-024-71599-6
13.
CappellaJ. N.JamiesonK. H. (1997). Spiral of cynicism: The press and the public good. Oxford University Press.
14.
CareyJ. M.GuessA. M.LoewenP. J.MerkleyE.NyhanB.PhillipsJ. B.ReiflerJ. (2022). The ephemeral effects of fact-checks on COVID-19 misperceptions in the United States, Great Britain and Canada. Nature Human Behaviour, 6(2), 236–243. https://www.nature.com/articles/s41562-021-01278-3
15.
ChaikenS. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766. https://doi.org/10.1037/0022-3514.39.5.752
16.
ChaikenS.LedgerwoodA. (2012). A theory of heuristic and systematic information processing. In Van LangeP. A. M.KruglanskiA. W.HigginsE. T. (Eds.), Handbook of theories of social psychology (Vol. 1, pp. 246–266). Sage. https://doi.org/10.4135/9781446249215.n13
17.
ChanM. P. S.JonesC. R.Hall JamiesonK.AlbarracínD. (2017). Debunking: A meta-analysis of the psychological efficacy of messages countering misinformation. Psychological Science, 28(11), 1531–1546. https://doi.org/10.1177/0956797617714579
18.
ChenL.ZhangZ.ZhangX.ZhaoL. (2022). A pilot study for understanding users’ attitudes towards a conversational agent for news recommendation. In Proceedings of the 4th conference on conversational user interfaces, Glasgow, United Kingdom, 26–28 July 2022, pp. 1–6. https://doi.org/10.1145/3543829.3544530
19.
ChenS.ChaikenS. (1999). The Heuristic–Systematic Model in its broader context. In ChaikenS.TropeY. (Eds.), Dual-process theories in social psychology (pp. 73–96). Guilford Press.
20.
ChanM.LeeF. L.ChenH. T. (2024). Avoid or authenticate? A multilevel cross-country analysis of the roles of fake news concern and news fatigue on news avoidance and authentication. Digital Journalism, 12(3), 356–375.
21.
DouglasK. M.SuttonR. M.Van LissaC. J.StroebeW.KreienkampJ.AgostiniM.BélangerJ. J.GützkowB.AbakoumkinG.Abdul KhaiyomJ. H.AhmediV.AkkasH.AlmenaraC. A.AttaM.BagciS. C.BaselS.Berisha KidaE.BernardoA. B. I.ButtrickN. R., . . . LeanderN. P. (2023). Identifying important individual-and country-level predictors of conspiracy theorizing: A machine learning analysis. European Journal of Social Psychology, 53(6), 1191–1203. https://doi.org/10.1002/ejsp.2968
GriffinR. J.NeuwirthK.GieseJ.DunwoodyS. (2002). Linking the heuristic-systematic model and depth of processing. Communication Research, 29(6), 705–732. https://doi.org/10.1177/009365002237833
HarambamJ. (2023). The proliferation of alternative media: How corona conspiracy theories in the Netherlands fostered new social movements. In ButterM.KnightP. (Eds.), Covid conspiracy theories in global perspective (pp. 252–267). Routledge.
26.
HelbergerN. (2021). On the democratic role of news recommenders. In ThurmanN.LewisS. C.KunertJ. (Eds.), Algorithms, automation, and news (pp. 14–33). Routledge.
27.
HimelboimI.BorahP.LeeD. K. L.LeeJ.SuY.VishnevskayaA.XiaoX. (2024). What do 5G networks, Bill Gates, Agenda 21, and QAnon have in common? Sources, distribution, and characteristics. New Media & Society, 26(10), 6019–6039.
HuangS.WangS. (2023). Is artificial intelligence more persuasive than humans? A meta-analysis. Journal of Communication, 73(6), 552–562. https://doi.org/10.1093/joc/jqad024
30.
JakeschM.FrenchM.MaX.HancockJ. T.NaamanM. (2019, May). AI-mediated communication: How the perception that profile text was written by AI affects trustworthiness. In Proceedings of the 2019 CHI conference on human factors in computing systems, Glasgow, United Kingdom, 4–9 May 2019, pp. 1–13. https://doi.org/10.1145/3290605.3300469
31.
JostJ. T.BaldassarriD. S.DruckmanJ. N. (2022). Cognitive–motivational mechanisms of political polarization in social-communicative contexts. Nature Reviews Psychology, 1(10), 560–576. https://doi.org/10.1038/s44159-022-00093-5
32.
KimE.ShoenbergerH.SunY. (2021). Living in a material world: Sponsored Instagram posts and the role of materialism, hedonic enjoyment, perceived trust, and need to belong. Social Media+ Society, 7(3), 1–11. https://doi.org/10.1177/20563051211038306
33.
KingG.PanJ.RobertsM. E. (2017). How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. American Political Science Review, 111(3), 484–501. https://doi.org/10.1017/S0003055417000144
34.
Kleis NielsenR.GanterS. A. (2018). Dealing with digital intermediaries: A case study of the relations between publishers and platforms. New Media & Society, 20(4), 1600–1617. https://doi.org/10.1177/1461444817701318
35.
Knobloch-WesterwickS.SharmaN.HansenD. L.AlterS. (2005). Impact of popularity indications on readers’ selective exposure to online news. Journal of Broadcasting & Electronic Media, 49(3), 296–313. https://doi.org/10.1207/s15506878jobem4903_3
36.
KochT. K.FrischlichL.LermerE. (2023). Effects of fact-checking warning labels and social endorsement cues on climate change fake news credibility and engagement on social media. Journal of Applied Social Psychology, 53, 495–507. https://doi.org/10.1111/jasp.12959
37.
KostkaG.Habich-SobiegallaS. (2024). In times of crisis: Public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448221083285
38.
LeeD. K. L.RamazanO. (2021). Fact-checking of health information: The effect of media literacy, metacognition and health information exposure. Journal of Health Communication, 26(7), 491–500. https://doi.org/10.1080/10810730.2021.1955312
39.
LeeF. L. F.ChanJ. M. (2018). Media and protest logics in the digital era: The Umbrella Movement in Hong Kong. Oxford University Press.
40.
LeeF. L. F.ChengE. W.LiangH.TangG. K.YuenS. (2022). Dynamics of tactical radicalisation and public receptiveness in Hong Kong’s Anti-Extradition Bill Movement. Journal of Contemporary Asia, 52(3), 429–451.
41.
LewandowskyS.EckerU. K. H.SeifertC. M.SchwarzN.CookJ. (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.
LewandowskyS.OberauerK.GignacG. E. (2013). NASA faked the moon landing—therefore, (climate) science is a hoax: An anatomy of the motivated rejection of science. Psychological Science, 24(5), 622–633. https://doi.org/10.1177/0956797612457686
43.
LiN.StroudN. J.JamiesonK. H. (2017). Overcoming false causal attribution: Debunking the MMR–autism association. In The Oxford handbook of the science of science communication (pp. 433–444). Oxford University Press.
44.
LiaoM. (2023). Understanding the effects of personalized recommender systems on political news perceptions: A comparison of content-based, collaborative, and editorial choice-based news recommender system. Journal of Broadcasting & Electronic Media, 67(1), 1–29. https://doi.org/10.1080/08838151.2023.2206662
45.
LinW.-Y.CaoB.ZhangX. (2017). To speak or not to speak: Predicting college students’ outspokenness in the pro-democracy movement in Hong Kong. International Journal of Communication, 11, 3704–3720. https://ijoc.org/index.php/ijoc/article/view/6723/2137
46.
LuptonD. (2019). “I’d like to think you could trust the government, but I don’t really think we can”: Australian women’s attitudes to and experiences of My Health Record. Digital Health, 5, 2055207619847017.
47.
LyonsB.MérolaV.ReiflerJ.StoeckelF. (2020). How politics shape views toward fact-checking: Evidence from six European countries. The International Journal of Press/Politics, 25(3), 469–492. https://doi.org/10.1177/1940161220921732
48.
MadduxJ. E.RogersR. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19(5), 469–479. https://doi.org/10.1016/0022-1031(83)90023-9
49.
MahlD.SchäferM. S.ZengJ. (2023). Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research. New Media & Society, 25(7), 1781–1801. https://doi.org/10.1177/14614448221075759
MbungeE.DzinamariraT.FashotoS. G.BataniJ. (2021). Emerging technologies and COVID-19 digital vaccination certificates and passports. Public Health in Practice, 2, 100136.
McPhersonM.Smith-LovinL.CookJ. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
54.
MerkleyE.SteculaD. A. (2018). Party elites or manufactured doubt? The informational context of climate change polarization. Science Communication, 40(2), 258–274.
55.
MetzgerM. J.FlanaginA. J.MeddersR. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439. https://doi.org/10.1111/j.1460-2466.2010.01488.x
56.
MinozziW.SongH.LazerD. M.NebloM. A.OgnyanovaK. (2020). The incidental pundit: Who talks politics with whom, and why?American Journal of Political Science, 64(1), 135–151. https://doi.org/10.1111/ajps.12469
NyhanB.ReiflerJ. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303–330. https://doi.org/10.1007/s11109-010-9112-2
59.
PalA.ChuaA. Y.GohD. H. L. (2019). Debunking rumors on social media: The use of denials. Computers in Human Behavior, 96, 110–122. https://doi.org/10.1016/j.chb.2019.02.022
60.
PetersenM. B.BorA.JørgensenF.LindholtM. F. (2021). Transparent communication about negative features of COVID-19 vaccines decreases acceptance but increases trust. Proceedings of the National Academy of Sciences, 118(29), e2024597118.
PinkletonB. E.AustinE. W.ZhouY.WilloughbyJ. F.ReiserM. (2012). Perceptions of news media, external efficacy, and public affairs apathy in political decision making and disaffection. Journalism & Mass Communication Quarterly, 89(1), 23–39. https://doi.org/10.1177/1077699011428586
63.
PrzeworskiA.TeuneH. (1970). The logic of comparative social inquiry. John Wiley.
64.
RauchfleischA.KaiserJ. (2020). The German far-right on YouTube: An analysis of user overlap and user comments. Journal of Broadcasting & Electronic Media, 64(3), 373–396. https://doi.org/10.1080/08838151.2020.1799690
65.
RosenstockI. M. (1974). The health belief model and preventive health behavior. Health Education Monographs, 2(4), 354–386. https://doi.org/10.1177/1090198174002004
66.
SantiniR. M.SallesD.BarrosC. E. (2022). We love to hate George Soros: A cross-platform analysis of the Globalism conspiracy theory campaign in Brazil. Convergence, 28(4), 983–1006. https://doi.org/10.1177/13548565221085833
67.
SavolainenR. (2022). What drives people to prefer health-related misinformation? The viewpoint of motivated reasoning. Information Research, 27(2). https://doi.org/10.47989/irpaper927
SundarS. S.JiaH.WaddellF.HuangY. (2015). Toward a theory of interactive media effects (TIME): Four models for explaining how interface features affect user psychology. In SundarS. S. (Ed.), The handbook of the psychology of communication technology (pp. 47–86). John Wiley & Sons, Inc.
70.
SundarS. S.KimJ. (2019). Machine heuristic: When we trust computers more than humans with our personal information. In Proceedings of the 2019 conference on human factors in computing systems proceedings (CHI’ 19), Glasgow, United Kingdom, Paper No. 538. https://doi.org/10.1145/3290605.3300768
71.
ThorsonK.CotterK.MedeirosM.PakC. (2021). Algorithmic inference, political interest, and exposure to news and politics on Facebook. Information Communication and Society, 24(2), 183–200. https://doi.org/10.1080/1369118X.2019.1642934
72.
Van der MeerT. G.JinY. (2020). Seeking formula for misinformation treatment in public health crises: The effects of corrective information type and source. Health Communication, 35(5), 560–575. https://doi.org/10.1080/10410236.2019.1573295
73.
Van DijckJ.PoellT.De WaalM. (2018). The platform society: Public values in a connective world. Oxford University Press.
74.
WalterA. S.DrochonH. (2022). Conspiracy thinking in Europe and America: A comparative study. Political Studies, 70(2), 483–501. https://doi.org/10.1177/0032321720972616
75.
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
76.
WalterN.TukachinskyR. (2020). 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, 47(2), 155–177. https://doi.org/10.1177/0093650219854600
77.
WangS.ChuT. H.HuangG. (2023). Do bandwagon cues affect credibility perceptions? A meta-analysis of the experimental evidence. Communication Research, 50(6), 720–744.
78.
WinterS.BrücknerC.KrämerN. C. (2015). They came, they liked, they commented: Social influence on Facebook News Channels. Cyberpsychology, Behavior, and Social Networking, 18(8), 431–436. https://doi.org/10.1089/cyber.2015.0005
79.
ZaroualiB.Van den BroeckE.WalraveM.PoelsK. (2018). Predicting consumer responses to a chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491–497. https://doi.org/10.1089/cyber.2017.0518
80.
ZhangX. (2023). Will political disagreement silence political expression? The role of information repertoire filtration and discussion network heterogeneity. Human Communication Research, 49(2), 139–148. https://doi.org/10.1093/hcr/hqad009
81.
ZhangX.LinW.-Y.DuttonW. H. (2022). The political consequences of online disagreement: The filtering of communication networks in a polarized political context. Social Media + Society, 8(3), 1–14. https://doi.org/10.1177/20563051221114391
82.
ZhaoX.ChenL.JinY.ZhangX. (2023). Comparing button-based chatbots with webpages for presenting fact-checking results: A case study of health information. Information Processing & Management, 60(2), 103203. https://doi.org/10.1016/j.ipm.2022.103203
83.
ZhuJ. H.WeaverD.LoV. H.ChenC.WuW. (1997). Individual, organizational, and societal influences on media role perceptions: A comparative study of journalists in China, Taiwan, and the United States. Journalism & Mass Communication Quarterly, 74(1), 84–96. https://doi.org/10.1177/107769909707400107
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