The DOI matches its Crossref record.
YouTube Monetization and Censorship by Proxy: A Machine Learning Prospective
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In the citing paper
“As creators gain visibility, they attract further attention through algorithmic amplification (Zappin et al. 2022; Hua et al. 2022), creating a self-reinforcing feedback loop that concentrates income among a small group of highly popular creators - a 'winner takes all' or viral effect (Rosen 1981; Rieder et al. 2018).”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as Zappin et al. 2022
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Citations
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Research works printed but not linked: 3
- Jin Li, “ Predicting Large-Scale Internet Censorship – A Machine Learning Approach”, Thesis, aviable at: https://libra2.lib.virginia.edu/downloads/d504rk52c?filename=Jin_Li_MS_Thesis.pdf. Downloaded 9th January 2021.
- Christopher Cross, “Bypassing Censorship Reverse Engineering a Social Media Censorship Classifier to Generate Adversarial Posts”, in procedings of Cross BypassingCR, 2019.
- Baehrens, 2010, How to Explain Individual Classification Decisions, JMLR, Vol., 11, 2012
Cited by
Sorted by citations
"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data
Strauss, I., Yang, J., & Mazzucato, M. (2025) · arXiv
Metadata
- Authors
- Zappin, A., Malik, H., Shakshuki, E. M., & Dampier, D. A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 198
- Pages
- 23-32
- ISSN
- 1877-0509
- Cited by
- 14
- References
- 0
- Language
- en
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