This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias
Open accessAbstract
During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the effect of task on interaction with attitude-confirming search results. We conducted a preregistered, between-subjects crowdsourced user study (N=328) comparing six groups: three levels of obfuscation (targeted, random, none) and two levels of task (joint, two separate) for four debated topics. We found that both types of obfuscation influence user interactions, and in particular that targeted obfuscation helps decrease interaction with attitude-confirming search results. Future work is needed to understand how much of the observed effect is due to the strong influence of obfuscation, versus the warning label or the task design. We discuss design guidelines concerning system goals such as decreasing consumption of attitude-confirming search results, versus nudging users toward a more analytical mode of information processing. We also discuss implications for future work, such as the effects of interventions for confirmation bias mitigation over repeated exposure. We conclude with a strong word of caution: measures such as obfuscations should only be used for the benefit of the user, e.g., when they explicitly consent to mitigating their own biases.
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Task difficulty significantly impacts user reliance on AI suggestions.
“While task difficulty is not typically considered in studies on AI explanations [63], our results show that it significantly impacts user reliance on AI suggestions.”
From Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations (2025) · cites this paper as [63]
Cites
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The Elaboration Likelihood Model of Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · Communication and Persuasion · doi:10.1007/978-1-4612-4964-1_1 · Cited by 3,202 · see citing works
Misinformation and Its Correction: Continued Influence and Successful Debiasing
Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012) · Psychological Science in the Public Interest · doi:10.1177/1529100612451018 · Cited by 2,920 · see citing works
Deconstructing the Diversity Principle
Napoli, P. M. (1999) · Journal of Communication · doi:10.1111/j.1460-2466.1999.tb02815.x · Cited by 370 · see citing works
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A Checklist to Combat Cognitive Biases in Crowdsourcing
Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v9i1.18939 · Cited by 86 · see citing works
Self-citationBach, A. K. P., Nørgaard, T. M., Brok, J. C., & van Berkel, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581513 · Cited by 47 · see citing works
de Jong, S., Paananen, V., Tag, B., & van Berkel, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710946 · Cited by 32 · see citing works
Rosbach, E., Ammeling, J., Krügel, S., Kießig, A., Fritz, A., Ganz, J., Puget, C., Donovan, T., Klang, A., Köller, M. C., et al. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713319 · Cited by 18 · see citing works
- Authors
- Rieger, A., Draws, T., Theune, M., & Tintarev, N.
- Affiliations
- Delft University of Technology (NL)
- University of Twente (NL)
- Maastricht University (NL)
- Venue
- Proceedings of the 32st ACM Conference on Hypertext and Social Media
- Publisher
- ACM
- Published
- 2021-08-25
- Pages
- 189-199
- OpenAlex
- W3193320751
- Cited by
- 48
- References
- 4
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 15 · 2023: 18 · 2022: 4 · 2021: 1
- FWCI
- 7.88
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Hypertext and Social Media (HT) (publishedVersion, cc-by)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- University of Twente Research Information (publishedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- University of Twente Research Information (submittedVersion, other-oa)
- Funders
- Horizon 2020 Framework Programme (86062)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- confirmation bias, warning labels, web information retrieval, task design, information seeking, bias mitigation, nudging, user consent
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