Assessing the bias in samples of large online networks
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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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A Survey on Bias and Fairness in Machine Learning
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Fairness in machine learning: A survey
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The Critical Periphery in the Growth of Social Protests
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Fairness in Machine Learning: A Survey
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
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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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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.
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