Digitising “The Big Lie”: Algorithmic Curation as an Inhibitor of Media Exposure Diversity Online
Open accessAbstract
While multiple scholarly disciplines have scrutinised algorithms and their social power, this article examines algorithmic recommender systems in terms of their potential to reduce exposure diversity online.The paper positions the debate on algorithmic content curation within the theoretical framework of media diversity studies, emphasising the normative ideal of media diversity as a social good, and examining the impact of algorithmic behaviour on personal autonomy, specifically focusing on exposure diversity in online environments.The discussion then introduces a complementary concept to Pariser's (2011) notion of the "filter bubble," namely the shrinkage funnel, and discusses this concept in terms of online exposure diversity, personal choice sovereignty, and its potential to initiate the illusory truth effect.The paper reviews selected examples of content restrictive algorithmic behaviour to demonstrate how shrinkage funnels can undermine personal autonomy and prompt detrimental societal or political outcomes.By distinguishing between filter bubbles and shrinkage funnels, this study provides a foundation for future empirical investigations and potential policy interventions aimed at promoting exposure diversity in algorithmically curated online environments.
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In algorithmically curated reality, self-narratives become increasingly shaped by machine-generated frameworks.
“In algorithmically curated reality, however, self-narratives become increasingly shaped by machine-generated frameworks [29].”
From Encountering Generative AI: Narrative Self-Formation and Technologies of the Self Among Young Adults (2026) · cites this paper as [29]
The ‘Context and Background: Algorithms and Media Diversity Online’ section says machine-curated content and AI-informed recommendations guide users’ choices. The ‘Theoretical Framework’ section also describes how restricted exposure can reinforce views of the world. Neither passage says that people’s self-narratives are shaped by machine-generated frameworks; that extension is unsupported. Page numbers are unavailable in our copy. (LLM, conf=0.83)
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Cites
Deep Neural Networks for YouTube Recommendations
Covington, P., Adams, J., & Sargin, E. (2016) · Proceedings of the 10th ACM Conference on Recommender Systems · doi:10.1145/2959100.2959190 · Cited by 3,539 · see citing works
Age of Surveillance Capitalism – The Fight for a Human Future at the New Frontier of Power.
Kendell, D. (2020) · Brock Education Journal · doi:10.26522/brocked.v29i2.849 · Cited by 2,159 · see citing works
FILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849 · see citing works
(2003) · Routledge eBooks · doi:10.4324/9780203381182 · Cited by 1,740 · see citing works
The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism
Couldry, N., & Mejias, U. A. (2020) · doi:10.1515/9781503609754 · Cited by 1,246 · see citing works
Thinking critically about and researching algorithms
Kitchin, R. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1154087 · Cited by 1,189 · see citing works
The echo chamber is overstated: the moderating effect of political interest and diverse media
Dubois, E., & Blank, G. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1428656 · Cited by 1,055 · see citing works
The social power of algorithms
Beer, D. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1216147 · Cited by 945 · cited 1× · see citing works
“…truth effect has surfaced in research related to the social power of algorithms and their impact on the perception of truth, on misinformation, the spread of conspiracy theories online, and the potential of utilising digital platforms to impact election outcomes (Beer 2017; Vellani et al. 2023).”
‘Hypernudge’: big Data as a mode of regulation by design
Yeung, K. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1186713 · Cited by 868 · see citing works
Exploring the filter bubble: the effect of using recommender systems on content diversity
Nguyen, T. T., Hui, P.-M., Harper, F., Terveen, L., & Konstan, J. (2014) · The Web Conference · doi:10.1145/2566486.2568012 · Cited by 690 · see citing works
Fake news and ideological polarization: Filter bubbles and selective exposure on social media
Spohr, D. (2017) · Business Information Review · doi:10.1177/0266382117722446 · Cited by 530 · see citing works
Burst of the Filter Bubble?: Effects of personalization on the diversity of <i>Google News</i>
Haim, M., Graefe, A., & Brosius, H.-B. (2018) · Digital Journalism · doi:10.1080/21670811.2017.1338145 · Cited by 399 · see citing works
Breaking the filter bubble: democracy and design
Bozdag, E., & van den Hoven, J. (2015) · Ethics and Information Technology · doi:10.1007/s10676-015-9380-y · Cited by 375 · 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
Exposure diversity as a design principle for recommender systems
Helberger, N., Karppinen, K., & D’Acunto, L. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1271900 · Cited by 368 · see citing works
COVID-19-related misinformation on social media: a systematic review
Gabarron, E., Oyeyemi, S. O., & Wynn, R. (2021) · Bulletin of the World Health Organization · doi:10.2471/blt.20.276782 · Cited by 345 · see citing works
Brashier, N. M., & Marsh, E. J. (2020) · Annual Review of Psychology · doi:10.1146/annurev-psych-010419-050807 · Cited by 288 · see citing works
Users Polarization on Facebook and Youtube
Bessi, A., Zollo, F., Del Vicario, M., Puliga, M., Scala, A., Caldarelli, G., Uzzi, B., & Quattrociocchi, W. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159641 · Cited by 285 · see citing works
Rhodes, S. C. (2021) · Political Communication · doi:10.1080/10584609.2021.1910887 · Cited by 273 · see citing works
The effects of repetition frequency on the illusory truth effect
Hassan, A., & Barber, S. J. (2021) · Cognitive Research Principles and Implications · doi:10.1186/s41235-021-00301-5 · Cited by 261 · see citing works
Nechushtai, E., & Lewis, S. C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.043 · Cited by 237 · see citing works
Willson, M. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1200645 · Cited by 205 · see citing works
Algorithmic regulation: A maturing concept for investigating regulation of and through algorithms
Ulbricht, L., & Yeung, K. (2021) · Regulation & Governance · doi:10.1111/rego.12437 · Cited by 125 · see citing works
Exposure Diversity Reconsidered
Napoli, P. M. (2011) · Journal of Information Policy · doi:10.5325/jinfopoli.1.2011.0246 · Cited by 117 · see citing works
Helberger, N. (2011) · Journal of Information Policy · doi:10.5325/jinfopoli.1.2011.0441 · Cited by 97 · see citing works
The illusory truth effect leads to the spread of misinformation
Vellani, V., Zheng, S., Ercelik, D., & Sharot, T. (2023) · Cognition · doi:10.1016/j.cognition.2023.105421 · Cited by 83 · see citing works
A reproducible systematic map of research on the illusory truth effect
Henderson, E. L., Westwood, S. J., & Simons, D. J. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01995-w · Cited by 57 · see citing works
Steiner, M., Magin, M., Stark, B., & Geiß, S. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1776367 · Cited by 48 · see citing works
Endogenetic structure of filter bubble in social networks
Min, Y., Jiang, T., Cheng, J., Li, Q., & Jin, X. (2019) · Royal Society Open Science · doi:10.1098/rsos.190868 · Cited by 41 · see citing works
Preference Amplification in Recommender Systems
Kalimeris, D., Bhagat, S., Kalyanaraman, S., & Weinsberg, U. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467298 · Cited by 30 · see citing works
Valcke, P. (2011) · Journal of Information Policy · doi:10.5325/jinfopoli.1.2011.0287 · Cited by 28 · see citing works
Duncan, J., & Reid, J. (2013) · Communicatio · doi:10.1080/02500167.2013.864448 · Cited by 22 · see citing works
Self-citationUser Behavior and Awareness of Filter Bubbles in Social Media
Plettenberg, N., Nakayama, J., Belavadi, P., Halbach, P., Burbach, L., Valdez, A. S. C., & Ziefle, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49907-5_6 · Cited by 15 · see citing works
Media Diversity from the User's Perspective: An Introduction
Helberger, N. (2011) · Journal of Information Policy · doi:10.5325/jinfopoli.1.2011.0241 · Cited by 10 · see citing works
Making a difference to media pluralism:
Karppinen, K. (2006) · Intellect Books · doi:10.2307/j.ctv36xvqrf.6 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Bergen, Mark. 2022. Like Comment Subscribe. Inside YouTube's Chaotic Rise to World Domination. New York: Penguin.
Cited by
Sorted by citations
The algorithmic self: how AI is reshaping human identity, introspection, and agency
Joseph, J. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1645795 · Cited by 63 · see citing works
Encountering Generative AI: Narrative Self-Formation and Technologies of the Self Among Young Adults
Kvietkute, D., & Ness, I. J. (2026) · Societies · doi:10.3390/soc16010026 · Cited by 5 · see citing works
“In algorithmically curated reality, however, self-narratives become increasingly shaped by machine-generated frameworks [29].”
Wu, M., Yong, K. M., & Yuen, K. F. (2026) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2026.100993 · Cited by 2 · see citing works
Pak, C., & Lee, S.-E. (2025) · Asian Communication Research · doi:10.20879/acr.2025.22.020
THE RIGHT TO FORGET: WHO CONTROLS OUR DIGITAL MEMORY AND MEDIA ARCHIVES IN THE AGE OF AI?
Cheriti, F., Kanoun, D., & Fridjat, N. (2026) · Metafizika Journal · doi:10.33864/2617-751x.2026.v9.i1.26-45
The Filter Market: A Speculative Interface for Buying and Performing Civility in Online Comments
Duarte, R., Antunes, M. J., & Lélis, C. (2025) · International Conference on Digital and Interactive Arts (ARTECH) · doi:10.1145/3773699.3773924
Bozdağ, A. A. (2026) · doi:10.1002/9781394375189.ch13
Duarte, R., Antunes, M. J., & Lélis, C. (2026) · Springer series in design and innovation · doi:10.1007/978-3-032-13836-1_40
Osife-Kurex, P. O., Ipori, L. O., Oghenetega, J. V., & Ajirioghene, O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7478278
P., S. C., & Shukla, R. (2026) · Frontiers in Sociology · doi:10.3389/fsoc.2026.1940431
- Authors
- Reid, J.
- Affiliations
- University of South Africa (ZA)
- Corresponding author
- Reid, Julie
- ORCID
- Reid, Julie
- Venue
- Communicatio
- Publisher
- Informa UK Limited
- Published
- 2024-10
- Volume
- 50
- Issue
- 4
- Pages
- 1-21
- ISSN
- 0250-0167 (ISSN-L)
- 1753-5379
- OpenAlex
- W4406334338
- Cited by
- 10
- References
- 35
- Citations by year
- 2026: 7 · 2025: 3
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
- Locations
- Communicatio (publishedVersion, public-domain)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- algorithmic curation, recommender systems, media pluralism, filter bubbles, individual autonomy, illusory truth effect, content restrictions, online media, algorithmic bias
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