Journal article
Searching for Affirmation: How Partisan Audiences on Google Search Induce an Abortion-Related Filter Bubble Effect
Proceedings of the ACM on human-computer interaction, Vol.9(7), pp.1-31
10/16/2025
DOI: 10.1145/3757656
Appears in UI Libraries Support Open Access
Abstract
The evolution of information-seeking processes, driven by search engines like Google, has reshaped how people access and interact with information. This paper examines how individuals' pre-existing attitudes on polarizing topics, such as the legality of abortion, influence their engagement with modern information-seeking processes. Recruiting participants from an undergraduate population of a university we use a mixed-methods study involving surveys and information-seeking tasks focused on the legality of abortion, this work offers five key insights. First, individuals with opposing abortion-related attitudes receive different search results. Second, the vocabulary used when formulated search queries differs significantly across opposing attitudes. Third, this difference in query vocabulary has a significant effect on the search results. Fourth, this effect remains consistent, though reduced, when personalization is removed from the process. Finally, Google Search returns search results that align with users' pre-existing attitudes, thereby reinforcing those attitudes. Taken together, these findings reveal a critical relationship between human biases, partisan audiences, and algorithmic processes. Specifically, our findings underscore how search platforms such as Google Search, which play a crucial role in the modern information-seeking process, contribute to information polarization.
Details
- Title: Subtitle
- Searching for Affirmation: How Partisan Audiences on Google Search Induce an Abortion-Related Filter Bubble Effect
- Creators
- Hussam Habib - University of IowaRyan Stoldt - Drake UniversityAndrew High - Pennsylvania State UniversityBrian Ekdale - University of IowaAshley Peterson - Pennsylvania State UniversityKaty Biddle - University of IowaJavie Ssozi - University of IowaRishab Nithyanand - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Proceedings of the ACM on human-computer interaction, Vol.9(7), pp.1-31
- DOI
- 10.1145/3757656
- ISSN
- 2573-0142
- eISSN
- 2573-0142
- Publisher
- Association for Computing Machinery (ACM)
- Number of pages
- 31
- Grant note
- #9550-20-1-0346 / Air Force Office of Scientific Research (https://doi.org/10.13039/100000181)
- Language
- English
- Date published
- 10/16/2025
- Academic Unit
- Center for Social Science Innovation; Computer Science; School of Journalism and Mass Communication; Law Faculty
- Record Identifier
- 9985014802002771
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