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Cited by 204Complex Network Analysis TechniquesRecommender Systems and TechniquesOpinion Dynamics and Social Influence

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How algorithmic popularity bias hinders or promotes quality

Open access
Ciampaglia, Giovanni Luca, Nematzadeh, Azadeh, Menczer, Filippo, Flammini, Alessandro·2018·Scientific Reports
DOI 10.1038/s41598-018-34203-2PMC PMC6206065
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Abstract

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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Crossref · 10.1038/s41598-018-34203-2

Cites

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Figures

Figure 1 Predicted average quality at equilibrium. Heatmap of average quality q¯ as a function of α and β, based on numerical solution of equilibrium condition derived in the text.
Figure 1 Predicted average quality at equilibrium. Heatmap of average quality q¯ as a function of α and β, based on numerical solution of equilibrium condition derived in the text.CC BY · PubMed Central
Figure 2 Effects of popularity bias on average quality and faithfulness from model simulations. (a) Heatmap of average quality q¯ as a function of α and β, showing that popularity bias can either hinder or promote average quality depending on exploration cost. (b) The location of the maximum q¯ as a function of β depends on α, here shown for α = 0, 1, 2. When α = 1 an intermediate amount of popularity bias is optimal. (c) Faithfulness τ of the algorithm as a function of α and β. (d) τ as a function of β for the same three values of α. Standard errors are shown in panels (b,d) and are smaller than the markers if not visible.
Figure 2 Effects of popularity bias on average quality and faithfulness from model simulations. (a) Heatmap of average quality q¯ as a function of α and β, showing that popularity bias can either hinder or promote average quality depending on exploration cost. (b) The location of the maximum q¯ as a function of β depends on α, here shown for α = 0, 1, 2. When α = 1 an intermediate amount of popularity bias is optimal. (c) Faithfulness τ of the algorithm as a function of α and β. (d) τ as a function of β for the same three values of α. Standard errors are shown in panels (b,d) and are smaller than the markers if not visible.CC BY · PubMed Central
Figure 3 Temporal evolution of average quality. Average quality q¯ is traced over time for β = 0.6 and different values of exploration cost. Error bars represent standard errors across runs. Compared to the optimal case α = 1, with more exploration (α = 0) the popularity bias just adds noise, and with less exploration (α = 2) it causes the system to converge prematurely to sub-optimal quality.
Figure 3 Temporal evolution of average quality. Average quality q¯ is traced over time for β = 0.6 and different values of exploration cost. Error bars represent standard errors across runs. Compared to the optimal case α = 1, with more exploration (α = 0) the popularity bias just adds noise, and with less exploration (α = 2) it causes the system to converge prematurely to sub-optimal quality.CC BY · PubMed Central

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Citations

Cites

1 retracted

Show all 32 references

Research works printed but not linked: 5

  1. Sunstein, C. R. Republic.com 2.0. Princeton university press (2009).
  2. Pariser, E. The filter bubble: How the new personalized Web is changing what we read and how we think. Penguin (2011).
  3. Adler, M. Stardom and talent. American economic review 75(1), 208–12 (1985).
  4. Dawkins, R. The selfish gene. Oxford university press (1989).
  5. Bell, E J., Owen, T., Brown, P D., Hauka, C. & Rashidian, N. The platform press: how Silicon Valley reengineered journalism. Technical report, Tow Center for Digital Journalism (2017).

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Metadata

Authors
Ciampaglia, G. L., Nematzadeh, A., Menczer, F., & Flammini, A.
Affiliations
Indiana University Bloomington (US)
Corresponding author
Ciampaglia, Giovanni Luca
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-10-29
Volume
8
Issue
1
Pages
15951-15951
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-018-34203-2
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
FWCI
9.32
Type
journal article
Language
en
Open access
Licence
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

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