We examined whether bypassing and combined (correction with bypassing) strategies delivered through artificial intelligence (AI) chatbots can address contraceptive misinformation. In Study 1, we found through observational reddit data that bypassing, correction, and combined strategies were present in online discussions and were more engaging than other responses. In Study 2, an experiment employing AI chatbots found that the combined strategy improved recommendation intentions compared with the control conditions, while also reducing beliefs in misinformation. The combined strategy AI chatbot was viewed as warmer and more competent than the correction, invoking less reactance and increasing recommendation intentions. Practical and theoretical implications are discussed.
AbonizioH. Q.da Costa BarbonA. P. A.RodriguesR.SantosM.Martínez-VizcaínoV.MesasA. E.JuniorS. B. (2023). How people interact with a chatbot against disinformation and fake news in COVID-19 in Brazil: The CoronaAI case. International Journal of Medical Informatics, 177, 105134. https://doi.org/10.1016/j.ijmedinf.2023.105134
2.
AggarwalA.TamC. C.WuD.LiX.QiaoS. (2023). Artificial intelligence–based chatbots for promoting health behavioral changes: Systematic review. Journal of Medical Internet Research, 25, e40789. https://doi.org/10.2196/40789
AnderssonK.BatarI.RyboG. (1992). Return to fertility after removal of a levonorgestrel-releasing intrauterine device and Nova-T. Contraception, 46(6), 575–584. https://doi.org/10.1016/0010-7824(92)90122-A
5.
AnthesG. (2017). Artificial intelligence poised to ride a new wave. Communications of the ACM, 60(7), 19–21. https://doi.org/10.1145/3088342
6.
BaileyM. J.HershbeinB.MillerA. R. (2012). The opt-in revolution? Contraception and the gender gap in wages. American Economic Journal: Applied Economics, 4(3), 225–254. https://doi.org/10.1257/app.4.3.225
7.
BlankJ. M.ShawD. (2015). Does partisanship shape attitudes toward science and public policy? The case for ideology and religion. The ANNALS of the American Academy of Political and Social Science, 658(1), 18–35. https://doi.org/10.1177/0002716214554756
8.
CaiN.GaoS.YanJ. (2024). How the communication style of chatbots influences consumers’ satisfaction, trust, and engagement in the context of service failure. Humanities and Social Sciences Communications, 11(1), 1–11. https://doi.org/10.1057/s41599-024-03212-0
9.
CalabreseC.AlbarracínD. (2023). Bypassing misinformation without confrontation improves policy support as much as correcting it. Scientific Reports, 13(1), 6005. https://doi.org/10.1038/s41598-023-33299-5
10.
CarnahanD.BerganD. E. (2022). Correcting the misinformed: The effectiveness of fact-checking messages in changing false beliefs. Political Communication, 39(2), 166–183. https://doi.org/10.1080/10584609.2021.1963358
11.
ChanM. P. S.AlbarracínD. (2023). A meta-analysis of correction effects in science-relevant misinformation. Nature Human Behaviour, 7(9), 1514–1525. https://doi.org/10.1038/s41562-023-01623-8
12.
ChanM. 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
CosmaC.RadiA.CattanoR.ZanobiniP.BonaccorsiG.LoriniC.Del RiccioM. (2025). Exploring Chatbot contributions to enhancing vaccine literacy and uptake: A scoping review of the literature. Vaccine, 44, 126559. https://doi.org/10.1016/j.vaccine.2024.126559
15.
CostelloT. H.PennycookG.RandD. G. (2024). Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), eadq1814. https://doi.org/10.1126/science.adq1814
16.
CuddyA. J. C.FiskeS. T.GlickP. (2008). Warmth and competence as universal dimensions of social perception: The stereotype content model and the BIAS map. In ZannaM. P. (Ed.), Advances in experimental social psychology (Vol. 40, pp. 61–149). Elsevier Academic Press. https://doi.org/10.1016/S0065-2601(07)00002-0
17.
DeVernaM. R.YanH. Y.YangK. C.MenczerF. (2024). Fact-checking information from large language models can decrease headline discernment. Proceedings of the National Academy of Sciences, 121(50), e2322823121. https://doi.org/10.1073/pnas.2322823121
18.
DillardJ. P.ShenL. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72(2), 144–168. https://doi.org/10.1080/03637750500111815
19.
EckerU. K.LewandowskyS.CookJ.SchmidP.FazioL. K.BrashierN.AmazeenM. 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
20.
FestingerL. (1957). A theory of cognitive dissonance. Row, Peterson.
21.
FishbeinM. (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, 16(3), 233–239. https://doi.org/10.1177/001872676301600302
22.
FishbeinM.AjzenI. (2011). Predicting and changing behavior: The reasoned action approach. Psychology Press.
23.
FiskeS. T.CuddyA. J. C.GlickP.XuJ. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82(6), 878–902. https://doi.org/10.1037/0022-3514.82.6.878
24.
GalloM. F.LopezL. M.GrimesD. A.CarayonF.SchulzK. F.HelmerhorstF. M. (2014). Combination contraceptives: Effects on weight. Cochrane Database of Systematic Reviews, 1, CD003987. https://doi.org/10.1002/14651858.CD003987.pub5
25.
GirumT.WasieA. (2018). Return of fertility after discontinuation of contraception: A systematic review and meta-analysis. Contraception and Reproductive Medicine, 3(1), 9. https://doi.org/10.1186/s40834-018-0064-y
26.
GongZ.SuL. Y. F. (2024). Exploring the influence of interactive and empathetic chatbots on health misinformation correction and vaccination intentions. Science Communication, 47(2), 276–308. https://doi.org/10.1177/10755470241280986
27.
Granados SamayoaJ. A.AlbarracínD. (2025). Bypassing versus correcting misinformation: Efficacy and fundamental processes. Journal of Experimental Psychology: General, 154(1), 18–38. https://doi.org/10.1037/xge0001687
28.
GrindlayK.GrossmanD. (2016). Prescription birth control access among US women at risk of unintended pregnancy. Journal of Women’s Health, 25(3), 249–254. https://doi.org/10.1089/jwh.2015.5312
29.
HoesE.AitkenB.ZhangJ.GackowskiT.WojcieszakM. (2024). Prominent misinformation interventions reduce misperceptions but increase scepticism. Nature Human Behaviour, 8(8), 1545–1553. https://doi.org/10.1038/s41562-024-01884-x
30.
HovG. G.SkjeldestadF. E.HilstadT. (2007). Use of IUD and subsequent fertility—follow-up after participation in a randomized clinical trial. Contraception, 75(2), 88–92. https://doi.org/10.1016/j.contraception.2006.09.010
31.
HuangY.LiuW. (2022). Promoting COVID-19 vaccination: The interplay of message framing, psychological uncertainty, and public agency as a message source. Science Communication, 44(1), 3–29. https://doi.org/10.1177/10755470211048192
32.
IlesI. A.GillmanA. S.PlatterH. N.FerrerR. A.KleinW. M. (2021). Investigating the potential of inoculation messages and self-affirmation in reducing the effects of health misinformation. Science Communication, 43(6), 768–804. https://doi.org/10.1177/10755470211048480
33.
JiJ.QinX.CalabreseC. (2025). Fact-checking misinformation on Chinese Social Media: Impact of corrections, awareness prompts, and legal warnings on endorsement. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448251334764
34.
JohnJ. N.GormanS.ScalesD.GormanJ. (2024). Online misleading information about women’s reproductive health: A narrative review. Journal of General Internal Medicine, 40, 1123–1131. https://doi.org/10.1007/s11606-024-09118-6
35.
JohnsonH. M.SeifertC. M. (1994). Sources of the continued influence effect: When misinformation in memory affects later inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(6), 1420–1436. https://doi.org/10.1037/0278-7393.20.6.1420
KimW. B.HurH. J. (2023). What makes people feel empathy for AI chatbots? Assessing the role of competence and warmth. International Journal of Human–computer Interaction, 40(17), 4674–4687. https://doi.org/10.1080/10447318.2023.2219961
38.
LaranjoL.DunnA. G.TongH. L.KocaballiA. B.ChenJ.BashirR.CoieraE. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072
LiZ.ShiJ. (2025). Message effects on psychological reactance: Meta-analyses. Human Communication Research, 52, 38–52. https://doi.org/10.1093/hcr/hqaf016
41.
LindhI.EllströmA. A.MilsomI. (2011). The long-term influence of combined oral contraceptives on body weight. Human Reproduction, 26(7), 1917–1924. https://doi.org/10.1093/humrep/der094
42.
LopezL. M.RameshS.ChenM.EdelmanA.OtternessC.TrussellJ.HelmerhorstF. M. (2016). Progestin-only contraceptives: Effects on weight. Cochrane Database of Systematic Reviews, 8, CD008815. https://doi.org/10.1002/14651858.CD008815.pub4
43.
MaR.MaZ.KalnyC. S.WalterN. (2025). Words that trigger: A meta-analysis of threatening language, reactance, and persuasion in health. Journal of Communication, 75, 348–357. https://doi.org/10.1093/joc/jqaf004
44.
MartelC.MoslehM.RandD. (2021). You’re definitely wrong, maybe: Correction style has minimal effect on corrections of misinformation online. Media and Communication, 9(1), 120–133. https://doi.org/10.17645/mac.v9i1.3519
MillsR.MangoneE. R.LeshN.JayalG.MohanD.BaraitserP. (2024). Chatbots that deliver contraceptive support: Systematic review. Journal of Medical Internet Research, 26, Article e46758. https://doi.org/10.2196/46758
47.
NabiR. L.Moyer-GuséE.ByrneS. (2007). All joking aside: A serious investigation into the persuasive effect of funny social issue messages. Communication Monographs, 74(1), 29–54. https://doi.org/10.1080/03637750701196896
48.
OhY. J.ZhangJ.FangM. L.FukuokaY. (2021). A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 160. https://doi.org/10.1186/s12966-021-01224-6
49.
OuM.HoS. S.WijayaS. A. (2025). Harnessing AI to address misinformation on cultivated meat: The impact of chatbot expertise and correction sidedness. Science Communication, 47, 859–896. https://doi.org/10.1177/10755470251315097
50.
PennycookG.EpsteinZ.MoslehM.ArecharA. A.EcklesD.RandD. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590–595. https://doi.org/10.1038/s41586-021-03344-2
51.
PettyR. E.CacioppoJ. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2
PornpitakpanC. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281. https://doi.org/10.1111/j.1559-1816.2004.tb02547.x
54.
QinJ.NanY.LiZ.MengJ. (2025). Effectiveness of communication competence in AI conversational agents for health: Systematic review and meta-analysis. Journal of Medical Internet Research, 27, Article e76296.
55.
RainieL. (2025). Close encounters of the AI kind: The increasingly human-like way people are engaging with language models. Imagining the Digital Future Center.
56.
RoozenbeekJ.van der LindenS. (Eds.). (2024). Interventions to combat misinformation. In RoozenbeekJ.van der LindenS. (Eds.), The psychology of misinformation (pp. 98–115). Cambridge University Press. https://doi.org/10.1017/9781009214414.011
SaucierC. J.CalabreseC.WalterN. (2025). Leveraging motivations to curb misinformation: Self-affirmation reduces the appeal of political conspiracy theories. Journal of Media Psychology: Theories, Methods, and Applications, 37(5), 316–322. https://doi.org/10.1027/1864-1105/a000481
59.
SmithC. N.SeitzH. H. (2019). Correcting misinformation about neuroscience via social media. Science Communication, 41(6), 790–819. https://doi.org/10.1177/1075547019890073
60.
SonfieldA.HasstedtK.KavanaughM. L.AndersonR. (2013). The social and economic benefits of women’s ability to determine whether and when to have children. Guttmacher Institute.
61.
SongH.McComasK. A.SchulerK. L. (2018). Source effects on psychological reactance to regulatory policies: The role of trust and similarity. Science Communication, 40(5), 591–620. https://doi.org/10.1177/1075547018791293
62.
SusmannM. W.WegenerD. T. (2022). The role of discomfort in the continued influence effect of misinformation. Memory & Cognition, 50(2), 435–448. https://doi.org/10.3758/s13421-021-01232-8
63.
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
64.
WalterN.BrooksJ. J.SaucierC. J.SureshS. (2020). Evaluating the impact of attempts to correct health misinformation on social media: A meta-analysis. Health Communication, 36(13), 1776–1784. https://doi.org/10.1080/10410236.2020.1794553
65.
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
66.
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
67.
WangJ.Tanes-EhleZ. (2023). Examining the effects of conversational chatbots on changing conspiracy beliefs about science: The paradox of interactivity. Journal of Broadcasting & Electronic Media, 67(1), 68–89. https://doi.org/10.1080/08838151.2022.2153842
XueH.ZhangJ.ZhangX. (2025). Facts or feelings? Leveraging emotionality as a fact-checking strategy on social media in the United States. Social Media + Society, 11(1). https://doi.org/10.1177/20563051251318172
70.
ZahryN. R.BesleyJ. C. (2021). Can scientists communicate interpersonal warmth? Testing warmth messages in the context of science communication. Journal of Applied Communication Research, 49(4), 387–405. https://doi.org/10.1080/00909882.2021.1880017
71.
ZhangJ.FeatherstoneJ. D.CalabreseC.WojcieszakM. (2021). Effects of fact-checking social media vaccine misinformation on attitudes toward vaccines. Preventive Medicine, 145, 106408. https://doi.org/10.1016/j.ypmed.2020.106408
72.
ZhangJ.OhY. J.LangeP.YuZ.FukuokaY. (2020). Artificial intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet. Journal of Medical Internet Research, 22(9), Article e22845. https://doi.org/10.2196/22845
