How Confidence in Prior Attitudes, Social Tag Popularity, and Source Credibility Shape Confirmation Bias Toward Antidepressants and Psychotherapy in a Representative German Sample: Randomized Controlled Web-Based Study

Stefan Schweiger, Ulrike Cress, Stefan Schweiger, Ulrike Cress

Abstract

Background: In health-related, Web-based information search, people should select information in line with expert (vs nonexpert) information, independent of their prior attitudes and consequent confirmation bias.

Objective: This study aimed to investigate confirmation bias in mental health-related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags eliminate the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility.

Methods: In total, 520 participants of a representative sample of the German Web-based population were recruited via a panel company. Among them, 48.1% (250/520) participants completed the fully automated study. Participants provided prior attitudes about antidepressants and psychotherapy. We manipulated (1) confidence in prior attitudes when participants searched for blog posts about the treatment of depression, (2) tag popularity -either psychotherapy or antidepressant tags were more popular, and (3) source credibility with banners indicating high or low expertise of the tagging community. We measured tag and blog post selection, and treatmentefficacy ratings after navigation.

Results: Tag popularity predicted the proportion of selected antidepressant tags (beta=.44, SE 0.11; P<.001) and blog posts (beta=.46, SE 0.11; P<.001). When confidence was low (-1 SD), participants selected more blog posts consistent with prior attitudes (beta=-.26, SE 0.05; P<.001). Moreover, when confidence was low (-1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude-consistent treatments increased (beta=.34, SE 0.13; P=.01).

Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health-related search context. That is, participants tended to select information that supported their prior attitudes, which is not in line with the current scientific evidence. Implications for presenting persuasive Web-based information are also discussed.

Trial registration: ClinicalTrials.gov NCT03899168; https://ichgcp.net/clinical-trials-registry/NCT03899168 (Archived by WebCite at http://www.webcitation.org/77Nyot3Do).

Keywords: Germany; antidepressive agents; attitude; consumer health information; culture; health literacy; information dissemination; information systems; professional competence; psychotherapy.

Conflict of interest statement

Conflicts of Interest: None declared.

©Stefan Schweiger, Ulrike Cress. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.04.2019.

Figures

Figure 1
Figure 1
The tag clouds used in the present study. Either psychotherapy (left), or antidepressants (right) were more popular.
Figure 2
Figure 2
CONSORT flow diagram. AD: antidepressants; PT: psychotherapy.
Figure 3
Figure 3
Experimental procedure.
Figure 4
Figure 4
Banner for the low source credibility condition.
Figure 5
Figure 5
Banner for the high source credibility condition.
Figure 6
Figure 6
Arguments for and against the 2 treatments.
Figure 7
Figure 7
Prior attitudes about psychotherapy and antidepressants before information search. AD: antidepressants; PT: psychotherapy.
Figure 8
Figure 8
Predicted proportion of antidepressant blog posts selected, for high (+1 SD) and low (−1 SD) confidence (95% CI), with negative values indicating a preference for antidepressants over psychotherapy.
Figure 9
Figure 9
Prior attitudes, confidence and source credibility, and treatment efficacy ratings after navigation, with negative values on all axes indicating a preference for antidepressants over psychotherapy. AD: antidepressants; PT: psychotherapy.

References

    1. Liao QV, Fu WT. Beyond the filter bubble: interactive effects of perceived threat and topic involvement on selective exposure to information. CHI '13 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; April 27-May 02, 2013; Paris, France. New York, NY: ACM; 2013. pp. 2359–68.
    1. Kimmerle J, Bientzle M, Cress U, Flemming D, Greving H, Grapendorf J, Sassenrath C, Sassenberg K. Motivated processing of health-related information in online environments. In: Buder J, Hesse F, editors. Informational Environments. Cham, Switzerland: Springer; 2017. pp. 75–96.
    1. Del Vicario M, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W. Modeling confirmation bias and polarization. Sci Rep. 2017 Dec 11;7:40391. doi: 10.1038/srep40391. doi: 10.1038/srep40391.
    1. Nickerson RS. Confirmation bias: a ubiquitous phenomenon in many guises. Rev Gen Psychol. 1998 Nov;2(2):175–220. doi: 10.1037/1089-2680.2.2.175.
    1. White RW, Horvitz E. Belief dynamics and biases in web search. ACM Trans Inf Syst. 2015 May 15;33(4):1–46. doi: 10.1145/2746229.
    1. Klayman J. Varieties of confirmation bias. Psychol Learn Motiv. 1995;32:385–418. doi: 10.1016/S0079-7421(08)60315-1.
    1. Hart W, Albarracín D, Eagly AH, Brechan I, Lindberg MJ, Merrill L. Feeling validated versus being correct: a meta-analysis of selective exposure to information. Psychol Bull. 2009 Jul;135(4):555–88. doi: 10.1037/a0015701.
    1. Wood W. Attitude change: persuasion and social influence. Annu Rev Psychol. 2000;51:539–70. doi: 10.1146/annurev.psych.51.1.539.
    1. Leary MR, Terry ML, Batts Allen A, Tate EB. The concept of ego threat in social and personality psychology: is ego threat a viable scientific construct? Pers Soc Psychol Rev. 2009 Aug;13(3):151–64. doi: 10.1177/1088868309342595.
    1. Jonas E, Schulz-Hardt S, Frey D. Giving advice or making decisions in someone else's place: the influence of impression, defense, and accuracy motivation on the search for new information. Pers Soc Psychol Bull. 2005 Jul 9;31(7):977–90. doi: 10.1177/0146167204274095.
    1. Moore DA, Healy PJ. The trouble with overconfidence. Psychol Rev. 2008 Apr;115(2):502–17. doi: 10.1037/0033-295X.115.2.502.
    1. Koriat A, Lichtenstein S, Fischhoff B. Reasons for confidence. J Exp Psychol Hum Learn Mem. 1980;6(2):107–18. doi: 10.1037/0278-7393.6.2.107.
    1. Zell E, Krizan Z. Do people have insight into their abilities? A metasynthesis. Perspect Psychol Sci. 2014 Mar;9(2):111–25. doi: 10.1177/1745691613518075.
    1. Park JH, Konana P, Gu B, Kumar A, Raghunathan R. Confirmation bias, overconfidence, and investment performance: evidence from stock message boards. oc Sci Res Netw Electron Pap Collect. 2010 Jul 12;:59. doi: 10.2139/ssrn.1639470.
    1. Rouault M, Seow T, Gillan CM, Fleming SM. Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biol Psychiatry. 2018 Sep 15;84(6):443–51. doi: 10.1016/j.biopsych.2017.12.017.
    1. Johnson DR, Murphy MP, Messer RM. Reflecting on explanatory ability: a mechanism for detecting gaps in causal knowledge. J Exp Psychol Gen. 2016 May;145(5):573–88. doi: 10.1037/xge0000161.
    1. Petty RE, Briñol P, Tormala ZL. Thought confidence as a determinant of persuasion: the self-validation hypothesis. J Pers Soc Psychol. 2002 May;82(5):722–41.
    1. Briñol P, Petty RE. Persuasion: insights from the self-validation hypothesis. In: Zanna MP, editor. Advances in Experimental Social Psychology. San Diego, CA, US: Elsevier Academic Press; 2009. pp. 69–118.
    1. Muchnik L, Aral S, Taylor SJ. Social influence bias: a randomized experiment. Science. 2013 Aug 9;341(6146):647–51. doi: 10.1126/science.1240466.
    1. Schweiger S, Oeberst A, Cress U. Confirmation bias in web-based search: a randomized online study on the effects of expert information and social tags on information search and evaluation. J Med Internet Res. 2014 Mar 26;16(3):e94. doi: 10.2196/jmir.3044.
    1. Cress U, Held C, Kimmerle J. The collective knowledge of social tags: direct and indirect influences on navigation, learning, and information processing. Comput Educ. 2013 Jan;60(1):59–73. doi: 10.1016/j.compedu.2012.06.015.
    1. Seitlinger P, Ley T, Albert D. An Implicit-Semantic Tag Recommendation Mechanism for Socio-Semantic Learning Systems. IFIP WG 3.4 International Conference on Open and Social Technologies for Networked Learning; July 30-August 3, 2012; Tallinn, Estonia. Berlin, Heidelberg: Springer; 2012. pp. 41–6.
    1. Ley T, Seitlinger P. Dynamics of human categorization in a collaborative tagging system: how social processes of semantic stabilization shape individual sensemaking. Comput Human Behav. 2015 Oct;51(Pt A):140–51. doi: 10.1016/j.chb.2015.04.053.
    1. Seitlinger P, Ley T, Albert D. Verbatim and Semantic Imitation in Indexing Resources on the Web: A Fuzzy-trace Account of Social Tagging. Appl. Cognit. Psychol. 2014 Aug 29;29(1):32–48. doi: 10.1002/acp.3067.
    1. Bateman S, Gutwin C, Nacenta M. Seeing things in the clouds: the effect of visual features on tag cloud selections. Proceedings of the nineteenth ACM conference on Hypertext and Hypermedia; June 19-21, 2008; Pittsburgh, PA, USA. 2008. pp. 193–202.
    1. Gligorov R, Hildebrand M, van Ossenbruggen J, Schreiber G, Aroyo L. On the role of user-generated metadata in audio visual collections. Proceedings of the sixth international conference on Knowledge capture; June 26-29, 2011; Banff, Alberta, Canada. 2011. pp. 145–52.
    1. Lohmann S, Ziegler J, Tetzlaff L. Comparison of tag cloud layouts: task-related performance and visual exploration. In: Gross T, editor. Human-Computer Interaction – INTERACT 2009. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2009. pp. 392–404.
    1. Kuo BY, Hentrich T, Good BM, Wilkinson MD. Tag clouds for summarizing web search results. Proceedings of the 16th international conference on World Wide Web; May 08-12, 2007; Banff, Alberta, Canada. New York, NY: ACM Press; 2007. pp. 1203–4.
    1. Cress U, Held C. Harnessing collective knowledge inherent in tag clouds. J Comput Assist Learn. 2012 Aug 23;29(3):235–47. doi: 10.1111/j.1365-2729.2012.00491.x.
    1. Held C, Kimmerle J, Cress U. Learning by foraging: the impact of individual knowledge and social tags on web navigation processes. Comput Human Behav. 2012 Jan;28(1):34–40. doi: 10.1016/j.chb.2011.08.008.
    1. Bohner G, Dykema-Engblade AD, Tindale RS, Meisenhelder H. Framing of Majority and Minority Source Information in Persuasion. Social Psychology. 2008 Jan;39(2):108–116. doi: 10.1027/1864-9335.39.2.108.
    1. Darke PR, Chaiken S, Bohner G, Einwiller S, Erb HP, Hazlewood JD. Accuracy Motivation, Consensus Information, and the Law of Large Numbers: Effects on Attitude Judgment in the Absence of Argumentation. Pers Soc Psychol Bull. 2016 Jul 02;24(11):1205–1215. doi: 10.1177/01461672982411007.
    1. Kammerer Y, Gerjets P. The role of search result position and source trustworthiness in the selection of web search results when using a list or a grid interface. Int J Human-Comput Interact. 2014 Jan 31;30(3):177–91. doi: 10.1080/10447318.2013.846790.
    1. Ma TJ, Atkin D. User generated content and credibility evaluation of online health information: a meta analytic study. Telemat Inform. 2017 Aug;34(5):472–86. doi: 10.1016/j.tele.2016.09.009.
    1. Yang Q, Beatty M. A meta-analytic review of health information credibility:belief in physicians or belief in peers? Health Inf Manag. 2016 Aug;45(2):80–9. doi: 10.1177/1833358316639432.
    1. Kammerer Y, Bråten I, Gerjets P, Strømsø HI. The role of Internet-specific epistemic beliefs in laypersons’ source evaluations and decisions during Web search on a medical issue. Comput in Human Behav. 2013 May;29(3):1193–1203. doi: 10.1016/j.chb.2012.10.012.
    1. O'Grady L, Wathen CN, Charnaw-Burger J, Betel L, Shachak A, Luke R, Hockema S, Jadad AR. The use of tags and tag clouds to discern credible content in online health message forums. Int J Med Inform. 2012 Jan;81(1):36–44. doi: 10.1016/j.ijmedinf.2011.10.001.
    1. Angermeyer MC, Matschinger H, Schomerus G. Attitudes towards psychiatric treatment and people with mental illness: changes over two decades. Br J Psychiatry. 2013 Aug;203(2):146–51. doi: 10.1192/bjp.bp.112.122978.
    1. Schomerus G, Schwahn C, Holzinger A, Corrigan PW, Grabe HJ, Carta MG, Angermeyer MC. Evolution of public attitudes about mental illness: a systematic review and meta-analysis. Acta Psychiatr Scand. 2012 Jun;125(6):440–52. doi: 10.1111/j.1600-0447.2012.01826.x.
    1. Angermeyer MC, Breier P, Dietrich S, Kenzine D, Matschinger H. Public attitudes toward psychiatric treatment. An international comparison. Soc Psychiatry Psychiatr Epidemiol. 2005 Nov;40(11):855–64. doi: 10.1007/s00127-005-0958-x.
    1. Jorm AF, Nakane Y, Christensen H, Yoshioka K, Griffiths KN, Wata Y. Public beliefs about treatment and outcome of mental disorders: a comparison of Australia and Japan. BMC Med. 2005 Jul 9;3:12. doi: 10.1186/1741-7015-3-12.
    1. Jorm A. Mental health literacy. Public knowledge and beliefs about mental disorders. Br J Psychiatry. 2000 Nov;177:396–401.
    1. Jorm AF, Christensen H, Griffiths KM. The public's ability to recognize mental disorders and their beliefs about treatment: changes in Australia over 8 years. Aust N Z J Psychiatry. 2006 Jan;40(1):36–41. doi: 10.1080/j.1440-1614.2006.01738.x.
    1. van Schaik DJ, Klijn A, van Hout HP, van Marwijk HW, Beekman A, de Haan M, van Dyck R. Patients' preferences in the treatment of depressive disorder in primary care. Gen Hosp Psychiatry. 2004;26(3):184–9. doi: 10.1016/j.genhosppsych.2003.12.001.
    1. Van der Auwera S, Schomerus G, Baumeister S, Matschinger H, Angermeyer M. Approval of psychotherapy and medication for the treatment of mental disorders over the lifespan. An age period cohort analysis. Epidemiol Psychiatr Sci. 2017 Dec;26(1):61–9. doi: 10.1017/S2045796015001134.
    1. Angermeyer MC, van der Auwera S, Carta MG, Schomerus G. Public attitudes towards psychiatry and psychiatric treatment at the beginning of the 21st century: a systematic review and meta-analysis of population surveys. World Psychiatry. 2017 Feb;16(1):50–61. doi: 10.1002/wps.20383. doi: 10.1002/wps.20383.
    1. De Maat S, Dekker J, Schoevers R, De Jonghe F. Relative efficacy of psychotherapy and pharmacotherapy in the treatment of depression: a meta-analysis. Psychother Res. 2006 Oct;16(5):566–78. doi: 10.1080/10503300600756402.
    1. Cuijpers P, Sijbrandij M, Koole SL, Andersson G, Beekman AT, Reynolds CF. The efficacy of psychotherapy and pharmacotherapy in treating depressive and anxiety disorders: a meta-analysis of direct comparisons. World Psychiatry. 2013 Jun;12(2):137–48. doi: 10.1002/wps.20038. doi: 10.1002/wps.20038.
    1. Amick HR, Gartlehner G, Gaynes BN, Forneris C, Asher GN, Morgan LC, Coker-Schwimmer E, Boland E, Lux LJ, Gaylord S, Bann C, Pierl CB, Lohr KN. Comparative benefits and harms of second generation antidepressants and cognitive behavioral therapies in initial treatment of major depressive disorder: systematic review and meta-analysis. Br Med J. 2015 Dec 8;351:h6019. doi: 10.1136/bmj.h6019.
    1. Menard S. Applied Logistic Regression Analysis. California: Sage; 2001. Oct 9, p. 128.
    1. Peng CY, Lee KL, Ingersoll GM. An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research. 2010 Apr 02;96(1):3–14. doi: 10.1080/00220670209598786.
    1. Van Breukelen GJ. ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies [corrected] J Clin Epidemiol. 2006 Sep;59(9):920–5. doi: 10.1016/j.jclinepi.2006.02.007.
    1. Aiken LS, West SG, Reno RR. Multiple Regression: Testing and Interpreting Interactions. California: Sage; 1991. Jan 1, p. 224.
    1. Sunstein CR. The ethics of nudging. SSRN J. 2014 Nov 20;:1–50. doi: 10.2139/ssrn.2526341.
    1. Quigley M. Nudging for health: on public policy and designing choice architecture. Med Law Rev. 2013;21(4):588–621. doi: 10.1093/medlaw/fwt022.
    1. Le Clainche C, Tubeuf S. Nudging, intervening or rewarding: a discussion on the constraints and the degree of control on health status. Polit Philos Econ. 2015 Sep 9;15(2):170–89. doi: 10.1177/1470594X15600832.
    1. Festinger L. Conflict, decision, and dissonance. Palo Alto, CA: Stanford University Press; 1964. p. 163.
    1. Harris PR, Napper L. Self-affirmation and the biased processing of threatening health-risk information. Pers Soc Psychol Bull. 2005 Sep;31(9):1250–63. doi: 10.1177/0146167205274694.
    1. van Koningsbruggen GM, Das E, Roskos-Ewoldsen DR. How self-affirmation reduces defensive processing of threatening health information: evidence at the implicit level. Health Psychol. 2009 Sep;28(5):563–8. doi: 10.1037/a0015610.
    1. Papakostas GI. Tolerability of modern antidepressants. J Clin Psychiatry. 2008;69(Suppl E1):8–13.
    1. Khawam EA, Laurencic G, Malone Jr DA. Side effects of antidepressants: an overview. Cleve Clin J Med. 2006 Apr;73(4):351–6.
    1. Statistisches Bundesamt. Wiesbaden, Hessen: Statistisches Bundesamt; 2013. Jahrbuch Deutschland.

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