How algorithmic popularity bias hinders or promotes quality
Open accessAbstract
Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.
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Optimizing for popularity seems to lower the overall quality of content.
“Optimizing for popularity even seems to lower the overall quality of content (Ciampaglia et al., 2018).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Ciampaglia et al., 2018
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Stray, J. (2023) · Analytics · doi:10.3390/analytics2020020 · Cited by 17 · see citing works
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Biases in Recommendation System
Bhadani, S. (2021) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3460231.3473897 · Cited by 15 · see citing works
Recommender Systems and Supplier Competition on Platforms
Fletcher, A., Ormosi, P. L., & Savani, R. (2023) · Journal of Competition Law & Economics · doi:10.1093/joclec/nhad009 · Cited by 17 · see citing works
Chung, M., & Wihbey, J. (2024) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-155 · Cited by 15 · see citing works
How Twitter data sampling biases U.S. voter behavior characterizations
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Self-citationSimulating News Recommendation Ecosystems for Insights and Implications
Zhang, G., Li, D., Gu, H., Lu, T., Shang, L., & Gu, N. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3381329 · Cited by 15 · see citing works
Modeling Language Usage and Listener Engagement in Podcasts
Reddy, S., Lazarova, M., Yu, Y., & Jones, R. (2021) · doi:10.18653/v1/2021.acl-long.52 · Cited by 16 · see citing works
Opinion formation on social networks with algorithmic bias: dynamics and bias imbalance
Peralta, A. F., Kertész, J., & Íñiguez, G. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac340f · Cited by 14 · see citing works
Analyzing ‘Near Me’ Services: Potential for Exposure Bias in Location-based Retrieval
Banerjee, A., Patro, G. K., Dietz, L. W., & Chakraborty, A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378476 · Cited by 13 · see citing works
Filter bubble effect in the multistate voter model
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Feng, Y., & Xie, Q. (2023) · Journal of Promotion Management · doi:10.1080/10496491.2023.2279768 · Cited by 14 · see citing works
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The Digital Misinformation Pipeline
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Upvote or Downvote ABA for Autism? Content and Support in Reddit Posts
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The effects of trust and influence on the spreading of low and high quality information
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T-RECS: A Simulation Tool to Study the Societal Impact of Recommender SystemsPreprint
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- Authors
- Ciampaglia, G. L., Nematzadeh, A., Menczer, F., & Flammini, A.
- Affiliations
- Indiana University Bloomington (US)
- Corresponding author
- Ciampaglia, Giovanni Luca
- Venue
- Scientific Reports
- Published
- 2018-10-29
- Volume
- 8
- Issue
- 1
- Pages
- 15951-15951
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6206065
- OpenAlex
- W2732691751
- Cited by
- 204
- References
- 32
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 41 · 2023: 25 · 2022: 25 · 2021: 33 · 2020: 27 · 2019: 8 · 2018: 5 · 2017: 4
- FWCI
- 9.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- IUScholarWorks Open (Indiana University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- James S. McDonnell Foundation (220020274)
- National Science Foundation (CCF-1101743)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Keywords
- popularity bias, algorithmic ranking, cultural market, exploration costs, user attention, recommendation algorithms, information quality, search engine ranking, citation ranking
- MeSH terms
- Algorithms, Quality Control, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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