Trust in scientists is a communicative construct shaped by public perception of scientists. Using network psychometrics on responses from 71,922 individuals in 68 countries, we model trust as an interconnected community system of attitudes and beliefs. Sincerity emerges as the most central node, linking perceptions of competence, integrity, and goodwill. This dynamic structure challenges reflective latent models and emphasizes trust as a product of message framing and credibility cues. Our findings inform science communication theory by reframing trust as an emergent network and offer practical guidance for communicators seeking to design inclusive, trust-enhancing strategies across diverse sociocultural contexts.
BenjaminiY.HochbergY. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B: Statistical Methodology, 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
3.
BernsteinE. E.PhillipsK. A.GreenbergJ. L.CurtissJ.HoeppnerS. S.WilhelmS. (2023). Mechanisms of cognitive-behavioral therapy effects on symptoms of body dysmorphic disorder: A network intervention analysis. Psychological Medicine, 53(6), 2531–2539. https://doi.org/10.1017/S0033291721004451
4.
BesleyJ. C.LeeN. M.PressgroveG. (2021). Reassessing the variables used to measure public perceptions of scientists. Science Communication, 43(1), 3–32. https://doi.org/10.1177/1075547020949547
5.
BesleyJ. C.TiffanyL. A. (2023). What are you assessing when you measure “trust” in scientists with a direct measure?Public Understanding of Science, 32(6), 709–726. https://doi.org/10.1177/09636625231161302
6.
BlankenT. F.Van Der ZweerdeT.Van StratenA.Van SomerenE. J. W.BorsboomD.LanceeJ. (2019). Introducing network intervention analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88(1), 52–54. https://doi.org/10.1159/000495045
7.
BorsboomD.DesernoM. K.RhemtullaM.EpskampS.FriedE. I.McNallyR. J.RobinaughD. J.PeruginiM.DalegeJ.CostantiniG.IsvoranuA.-M.WysockiA. C.Van BorkuloC. D.Van BorkR.WaldorpL. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 58. https://doi.org/10.1038/s43586-021-00055-w
8.
ChristensenA. P.GarridoL. E.Guerra-PeñaK.GolinoH. (2023). Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation. Behavior Research Methods, 56(3), 1485–1505. https://doi.org/10.3758/s13428-023-02106-4
9.
ChristensenA. P.GolinoH. (2021). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial. Psych, 3(3), 479–500. https://doi.org/10.3390/psych3030032
10.
ChristensenA. P.GolinoH.SilviaP. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34(6), 1095–1108. https://doi.org/10.1002/per.2265
11.
CieciuchJ.DavidovE. (2016). Establishing measurement invariance across online and offline samples. A tutorial with the software packages Amos and Mplus. Studia Psychologica, 15(2), 83. https://doi.org/10.21697/sp.2015.14.2.06
12.
ColognaV.MedeN. G.BergerS.BesleyJ.BrickC.JoubertM.MaibachE. W.MiheljS.OreskesN.SchäferM. S.Van Der LindenS.Abdul AzizN. I.AbdulsalamS.ShamsiN. A.AczelB.AdinugrohoI.AlabreseE.AldohA.AlfanoM.ZwaanR. A. (2025). Trust in scientists and their role in society across 68 countries. Nature Human Behaviour. https://doi.org/10.1038/s41562-024-02090-5
13.
De BenedictisL.RondinelliR.VinciottiV. (2023). Cultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances. Journal of the Royal Statistical Society: Series A (Statistics in Society), 186(3), 264–293. https://doi.org/10.1093/jrsssa/qnac007
14.
DinićB. M.CostantiniG.PapageorgiouK. A.Bonfá-AraujoB.GrabovacB.Żemojtel-PiotrowskaM.Ankit WertagA.KornienkoD.TripathiP.KyriazosT.ZhangJ.PogaM.TomaševićA. (2025). The Short Dark Triad across 14 cultures: A novel network-based invariance approach. Personality and Individual Differences, 246, 113321. https://doi.org/10.1016/j.paid.2025.113321
15.
EpskampS.WaldorpL. J.MõttusR.BorsboomD. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823
16.
FischerR.FontaineJ. R. J. (2011). Methods for investigating structural equivalence. In Cross-cultural research methods in psychology (pp. 179–215). Cambridge University Press.
17.
FriedE. I. (n.d.). Studying mental health problems as systems, not syndromes.
18.
FriedE. I.von StockertS.HaslbeckJ. M. B.LamersF.SchoeversR. A.PenninxB. W. J. H. (2020). Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychological Medicine, 50(16), 2682–2690. https://doi.org/10.1017/S0033291719002770
19.
GolinoH.MoulderR.ShiD.ChristensenA. P.GarridoL. E.NietoM. D.NesselroadeJ.SadanaR.ThiyagarajanJ. A.BokerS. M. (2021). Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research, 56(6), 874–902. https://doi.org/10.1080/00273171.2020.1779642
20.
GolinoH. F.EpskampS. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLOS ONE, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035
21.
HendriksF.KienhuesD.BrommeR. (2016). Trust in science and the science of trust. In: BlöbaumB. (eds) Trust and communication in a digitized world. Progress in IS. Springer.https://doi.org/10.1007/978-3-319-28059-2_8
22.
JamisonL.ChristensenA. P.GolinoH. F. (2024). Metric invariance in exploratory graph analysis via permutation testing. Methodology, 20(2), Article 2. https://doi.org/10.5964/meth.12877
23.
JiménezM.AbadF. J.Garcia-GarzonE.GolinoH.ChristensenA. P.GarridoL. E. (2025). Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach. Psychological Methods, 30(4), 770–792. https://doi.org/10.1037/met0000590
24.
LackoD.CenekJ.TočíkJ.AvsecA.ĐorđevićV.GencA.HakaF.Sakotic-KurbalijaJ.MohorićT.NeziriI.SuboticS. (2021). The necessity of testing measurement invariance in cross- cultural research: On potential bias in cross-cultural comparisons with individualism-collectivism self-report scales. https://doi.org/10.31234/osf.io/9ytj3
25.
LaskowskiN. M.HalbeisenG.BraksK.HuberT. J.PaslakisG. (2023). Exploratory graph analysis (EGA) of the dimensional structure of the eating disorder examination-questionnaire (EDE-Q) in women with eating disorders. Journal of Psychiatric Research, 163, 254–261. https://doi.org/10.1016/j.jpsychires.2023.05.063
26.
LewensteinB. V. (2024). Science communication in a diverse world. Journal of Science Communication, 23(05). https://doi.org/10.22323/2.23050401
27.
McCroskeyJ. C.TevenJ. J. (1999). Goodwill: A reexamination of the construct and its measurement. Communication Monographs, 66(1), 90–103. https://doi.org/10.1080/03637759909376464
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
30.
OpsahlT.AgneessensF.SkvoretzJ. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006
31.
PutnickD. L.BornsteinM. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
32.
ReifA.TaddickenM.GuentherL.SchröderJ. T.WeingartP. (2024). The Public Trust in Science Scale: A multilevel and multidimensional approach. Science Communication, 47(5), 670–701. https://doi.org/10.1177/10755470241302758
33.
RhemtullaM.FriedE. I.AggenS. H.TuerlinckxF.KendlerK. S.BorsboomD. (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence, 161, 230–237. https://doi.org/10.1016/j.drugalcdep.2016.02.005
34.
RoozenbeekJ.van der LindenS.GoldbergB.RathjeS.LewandowskyS. (2022). Psychological inoculation improves resilience against misinformation on social media. Science Advances, 8(34), eabo6254. https://doi.org/10.1126/sciadv.abo6254
35.
SapirE. V. (2022). Can we trust measures of trust? Measurement invariance in trust in EU news media. Sn Social Sciences, 2(10), 226. https://doi.org/10.1007/s43545-022-00534-1
36.
SaqrM.López-PernasS.TörmänenT.KaliisaR.MisiejukK.TikkaS. (2025). Transition network analysis: A novel framework for modeling, visualizing, and identifying the temporal patterns of learners and learning processes (No. arXiv:. 2411.15486). arXiv. https://doi.org/10.48550/arXiv.2411.15486
37.
SchröderJ. T.BrückJ.GuentherL. (2025). Identifying trust cues: How trust in science is mediated in content about science. Journal of Science Communication, 24(1). https://doi.org/10.22323/2.24010206
38.
StevensS. S. (1946). On the theory of scales of measurement. Science, New Series, 103(2684), 677–680.
39.
SturgisP.Brunton-SmithI.JacksonJ. (2021). Trust in science, social consensus and vaccine confidence. Nature Human Behaviour, 5(11), 1528–1534. https://doi.org/10.1038/s41562-021-01115-7
40.
TayeebwaD. W.WendoD. C.NakiwalaD. A. S. (2022). Theories and models of science communication. In Science communication skills for journalists (pp. 14–22). CABI. https://doi.org/10.1079/9781789249675.0002
41.
VragaE.MyersT.KotcherJ.BeallL.MaibachE. (2018). Scientific risk communication about controversial issues influences public perceptions of scientists’ political orientations and credibility. Royal Society Open Science, 5(2), 170505. https://doi.org/10.1098/rsos.170505
42.
WeingartP.GuentherL. (2016). Science communication and the issue of trust. Journal of Science Communication, 15(05), C01. https://doi.org/10.22323/2.15050301
43.
WilholtT. (2013). Epistemic trust in science. The British Journal for the Philosophy of Science, 64(2), 233–253. https://doi.org/10.1093/bjps/axs007
44.
WintterlinF.HendriksF.MedeN. G.BrommeR.MetagJ.SchäferM. S. (2022). Predicting public trust in science: The role of basic orientations toward science, perceived trustworthiness of scientists, and experiences with science. Frontiers in Communication, 6, 822757. https://doi.org/10.3389/fcomm.2021.822757
45.
WolffS. M.BreakwellG. M.WrightD. B. (2024). Psychometric evaluation of the Trust in Science and Scientists Scale. Royal Society Open Science, 11(4), 231228. https://doi.org/10.1098/rsos.231228
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