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This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias

Authors:

Mariët Theune,

Nava TintarevAuthors Info & Claims

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

Pages 189 - 199

https://doi.org/10.1145/3465336.3475101

Published: 29 August 2021 Publication History

Abstract

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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Cited By

Wang BLiu JOosterhuis HBast HXiong C(2024)Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated TopicsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672520(227-237)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672520
Starke AVrijenhoek SMichiels LKruse JTintarev N(2024)NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687103(1242-1244)Online publication date: 8-Oct-2024
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Jeon YKim JPark SKo YRyu SKim SHan K(2024)HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web SystemProceedings of the ACM on Human-Computer Interaction10.1145/36373408:CSCW1(1-34)Online publication date: 26-Apr-2024
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Index Terms

This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Search interfaces

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cover image ACM Conferences

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

August 2021

306 pages

ISBN:9781450385510

DOI:10.1145/3465336

General Chair:
Owen Conlan
Trinity College Dublin, Ireland
,
Program Chair:
Eelco Herder
Radboud Universiteit Nijmegen, Netherlands

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 29 August 2021

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August 30 - September 2, 2021

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Cited By

Wang BLiu JOosterhuis HBast HXiong C(2024)Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated TopicsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672520(227-237)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672520
Starke AVrijenhoek SMichiels LKruse JTintarev N(2024)NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687103(1242-1244)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687103
Jeon YKim JPark SKo YRyu SKim SHan K(2024)HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web SystemProceedings of the ACM on Human-Computer Interaction10.1145/36373408:CSCW1(1-34)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637340
Rieger ADraws TTheune MTintarev N(2024)Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. ManipulationACM Transactions on the Web10.1145/363503418:2(1-27)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3635034
Vrijenhoek SDaniil SSandel JHollink L(2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
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Bink MElsweiler D(2024)Balancing Act: Boosting Strategies for Informed Search on Controversial TopicsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638329(254-265)Online publication date: 10-Mar-2024
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McKay DMakri SBuchanan G(2024)[citation needed]: An Examination of Types and Purpose of Evidence Provided in Three Online Discussions on RedditProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638321(219-230)Online publication date: 10-Mar-2024
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Kruse JMichiels LStarke ATintarev NVrijenhoek S(2024)NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638319(422-424)Online publication date: 10-Mar-2024
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Rieger ABredius FTheune MPera M(2024)From Potential to Practice: Intellectual Humility During Search on Debated TopicsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638306(130-141)Online publication date: 10-Mar-2024
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Rieger AKulane SGadiraju UPera M(2024)Disentangling Web Search on Debated Topics: A User-Centered ExplorationProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659559(24-35)Online publication date: 22-Jun-2024
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