Cited by 238Complex Network Analysis TechniquesSocial Media and PoliticsComputational and Text Analysis Methods

Assessing the bias in samples of large online networks

González-Bailón, Sandra, Wang, Ning, Rivero, Alejandro, Borge-Holthoefer, Javier, Moreno, Yamir·2014·Social Networks

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Crossref · 10.1016/j.socnet.2014.01.004

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62 of 62 references verified0 citation errors · 0 not checkable

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  • Data veracity and quality, data relativity and context shifts, and subjectivity filters are challenges in fairness.

    “data veracity and quality [320], data relativity and context shifts [42,122,249], and subjectivity ilters [41].”

    From Fairness in machine learning: A survey (2024) · cites this paper as [122]

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Authors
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y.
Affiliations
  • University of Oxford (GB)
  • Universidad de Zaragoza (ES)
  • Institute for Scientific Interchange (IT)
Venue
Social Networks
Publisher
Elsevier BV
Published
2014-07
Volume
38
Pages
16-27
ISSN
0378-8733 (ISSN-L)
OpenAlex
W3122605581
Cited by
238
References
62
Citations by year
2026: 6 · 2025: 9 · 2024: 12 · 2023: 21 · 2022: 16 · 2021: 29 · 2020: 26 · 2019: 20 · 2018: 32 · 2017: 18 · 2016: 21 · 2015: 21 · 2014: 6
FWCI
14.48
Type
journal article
Language
en
Open access
closed
Locations
Social Networks (publishedVersion)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
sampling bias, social networking sites, Twitter, search API, snowball sampling, political protest, diffusion dynamics, political communication, network inference

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