Cited by 22Complex Network Analysis TechniquesEthics and Social Impacts of AIRecommender Systems and Techniques

Link recommendations: Their impact on network structure and minorities

Open access
Ferrara, Antonio, Espin-Noboa, Lisette, Karimi, Fariba, Wagner, Claudia·2022·14th ACM Web Science Conference 2022

Abstract

Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3501247.3531583

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Link recommendation algorithms have the potential to shape the overall evolution of social networks.

    “Link recommendation algorithms have the potential to shape the overall evolution of social networks 120-122 .”

    From Machine culture (2023) · cites this paper as [121]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Zadeh. 2013. Wtf: The who to follow service at twitter. In Proceedings of the 22nd international conference on World Wide Web. 505-514.
  2. Ronny Lempel and Shlomo Moran. 2001. SALSA: the stochastic approach for link-structure analysis. ACM Transactions on Information Systems (TOIS) 19, 2 (2001), 131-160.

Cited by

Sorted by citations

Authors
Ferrara, A., Espin-Noboa, L., Karimi, F., & Wagner, C.
Affiliations
  • GESIS - Leibniz Institute for the Social Sciences (DE)
  • Complexity Science Hub, Austria and Central European University, Austria
  • Complexity Science Hub (AT)
Venue
14th ACM Web Science Conference 2022
Publisher
ACM
Published
2022-06-26
Pages
228-238
OpenAlex
W4280495998
Cited by
22
References
37
Citations by year
2026: 4 · 2025: 3 · 2024: 6 · 2023: 7 · 2022: 2
FWCI
8.10
Type
conference paper
Language
en
Open access
Locations
  • ACM Web Science Conference (WebSci) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • Österreichische Forschungsförderungsgesellschaft (873927)
  • European Union?s Horizon 2020 (860630)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords
link recommendation, network structure, social bias, rich-get-richer effect, filter bubbles, polarization, minority groups, bipartite graphs, synthetic networks, 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 ›

Are you an author, or do you see a problem with this page? Request a review