Fake news and ideological polarization: Filter bubbles and selective exposure on social media
Abstract
This article addresses questions of ideological polarization and the filter bubble in social media. It develops a theoretical analysis of ideological polarization on social media by considering a range of relevant factors. Over recent years, fake news and the effect of the social media filter bubble have become of increasing importance both in academic and general discourse. The article reviews the assumption that algorithmic curation and personalization systems place users in a filter bubble of content that decreases their likelihood of encountering ideologically cross-cutting news content. At the intersection of new media, politics and behavioural science, the article establishes a theoretical framework for further research and future actions by society, policymakers and industries.
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Direct attempts by mainstream sources to confront and correct the highly biased worldviews of the partisan fringes, for instance through fact-checking initiatives, only confirm one's status as a critical outsider.
“direct attempts by mainstream sources to confront and correct the highly biased worldviews of the partisan fringes -for instance through fact-checking initiatives -only "confirm one's status as a critical outsider" (Krämer, 2017”
From Filter bubble (2019) · cites this paper as Spohr, 2017
There is clearly a market for political misinformation, and technology companies such as Twitter and Facebook profit from the spread of 'fake news', which increases customer engagement and therefore advertising revenue.
“there is clearly a market for political misinformation255, and technology companies such as Twitter and Facebook profit from the spread of ‘fake news’, which increases customer engagement and therefore advertising revenue256.”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [256]
Cites
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“The ease with which information on a given subject can be retrieved from memory has a direct effect on the decision-making process and the judgement of probabilities (Tversky and Kahneman, 1973).”
BackgroundSocial Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
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“Iyengar and others consider this trend of negative sentiment towards the opposing ideological group based on the concept of social distance (Bogardus, 1947) a better, ‘more diagnostic, indicator of mass polarisation’ than pure policy-based division and voting patterns (Iyengar et al., 2012, p. 406).”
BackgroundRed Media, Blue Media: Evidence of Ideological Selectivity in Media Use
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“Research from 2016 into the presence and severity of filter bubbles concluded ‘that – in spite of the serious concerns voiced – at present, there is no empirical evidence that warrants any strong worries about filter bubbles’ (Borgesius et al., 2016, p. 10).”
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Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120201 · Cited by 538 · see citing works
Cognitive–motivational mechanisms of political polarization in social-communicative contexts
Jost, J. T., Baldassarri, D. S., & Druckman, J. N. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00093-5 · Cited by 276 · see citing works
Bruns, A. (2019) · Internet Policy Review · doi:10.14763/2019.4.1426 · Cited by 193 · see citing works
De Paor, S., & Heravi, B. (2020) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2020.102218 · Cited by 191 · see citing works
Algorithmic audiencing: Why we need to rethink free speech on social media
Riemer, K., & Peter, S. (2021) · Journal of Information Technology · doi:10.1177/02683962211013358 · Cited by 66 · see citing works
Exploring the Use of Personalized AI for Identifying Misinformation on Social Media
Jahanbakhsh, F., Katsis, Y., Wang, D., Popa, L., & Muller, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581219 · Cited by 42 · see citing works
Kožuh, I., & Čakš, P. (2023) · Healthcare · doi:10.3390/healthcare11202796 · Cited by 21 · see citing works
North, S., Piwek, L., & Joinson, A. (2021) · Policy & Internet · doi:10.1002/poi3.247 · Cited by 16 · see citing works
Digitising “The Big Lie”: Algorithmic Curation as an Inhibitor of Media Exposure Diversity Online
Reid, J. (2024) · Communicatio · doi:10.1080/02500167.2024.2424841 · Cited by 10 · see citing works
- Authors
- Spohr, D.
- Affiliations
- Independent Scholar
- Corresponding author
- Spohr, Dominic
- Venue
- Business Information Review
- Publisher
- SAGE Publications
- Published
- 2017-08-23
- Volume
- 34
- Issue
- 3
- Pages
- 150-160
- ISSN
- 0266-3821 (ISSN-L)
- 1741-6450
- OpenAlex
- W2745338679
- Cited by
- 530
- References
- 6
- Citations by year
- 2026: 33 · 2025: 81 · 2024: 58 · 2023: 82 · 2022: 85 · 2021: 95 · 2020: 56 · 2019: 29 · 2018: 8 · 2017: 2 · 2014: 1
- FWCI
- 66.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Business Information Review (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- fake news, ideological polarization, filter bubbles, social media, algorithmic curation, personalization, echo chambers, news consumption, political behavior
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