Burst of the Filter Bubble?: Effects of personalization on the diversity of Google News
Abstract
In offering personalized content geared toward users’ individual interests, recommender systems are assumed to reduce news diversity and thus lead to partial information blindness (i.e., filter bubbles). We conducted two exploratory studies to test the effect of both implicit and explicit personalization on the content and source diversity of Google News. Except for small effects of implicit personalization on content diversity, we found no support for the filter-bubble hypothesis. We did, however, find a general bias in that Google News over-represents certain news outlets and under-represents other, highly frequented, news outlets. The results add to a growing body of evidence, which suggests that concerns about algorithmic filter bubbles in the context of online news might be exaggerated.
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There is scant empirical evidence for the existence of filter bubbles or echo chambers.
“Yet, there is scant empirical evidence for their existence, or for the related concept of 'echo chambers':”
From Filter bubble (2019) · cites this paper as Haim et al., 2018
Three major 2018 studies showed substantial overlaps in the search results seen by users of different political and other interests.
“In 2018 alone, three major studies showed substantial overlaps in the search results seen by users of different political and other interests:”
From Filter bubble (2019) · cites this paper as Haim et al., 2018
There is a severe lack of convincing evidence for the existence of significant filter bubbles in search results.
“in spite of Pariser's founding anecdote there is a severe lack of convincing evidence for the existence of significant filter bubbles in search results.”
From Filter bubble (2019) · cites this paper as Haim et al., 2018
For search, personalisation in general and news-specific search results appear to be exceptionally limited.
“at least for search, it seems that the filter bubble idea has deflated: far from the vision (or threat) of an individually unique Daily Me, personalisation in general and newsspecific search results still appears to be exceptionally limited.”
From Filter bubble (2019) · cites this paper as Haim et al., 2018
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References 36 · Cited by 399
- Authors
- Haim, M., Graefe, A., & Brosius, H.-B.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Hochschule Macromedia (DE)
- Corresponding author
- Haim, Mario
- Graefe, Andreas
- Brosius, Hans-Bernd
- Venue
- Digital Journalism
- Publisher
- Informa UK Limited
- Published
- 2017-07-06
- Volume
- 6
- Issue
- 3
- Pages
- 330-343
- ISSN
- 2167-0811 (ISSN-L)
- 2167-082X
- OpenAlex
- W2727940568
- Cited by
- 399
- References
- 36
- Citations by year
- 2026: 21 · 2025: 41 · 2024: 43 · 2023: 48 · 2022: 57 · 2021: 73 · 2020: 57 · 2019: 35 · 2018: 22 · 2017: 2
- FWCI
- 59.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Digital Journalism (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- filter bubbles, recommender systems, personalized news, news diversity, Google News, algorithmic bias
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