Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/40191

Measuring success in streaming platforms


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Title:
Measuring success in streaming platforms
Authors:
Gonçalves Dosantos, Juan Carlos
Martínez, Ricardo
Sánchez Soriano, Joaquín
Editor:
Elsevier
Department:
Departamentos de la UMH::Estadística, Matemáticas e Informática
Issue Date:
2025
URI:
https://hdl.handle.net/11000/40191
Abstract:
Digital streaming platforms , including Twitch, Spotify, Netflix, Disney+, and Kindle, have emerged as major sources of entertainment with significant growth potential. Many of these platforms distribute royalties among streamers, artists, producers, or writers based on their impact. In this paper, we measure the relevance of each of these contributors to the overall success of the platform, which can play a key role in revenue allocation. We perform an axiomatic analysis to provide normative foundations for four relevance metrics: the uniform, the subscriber-uniform, the proportional, and the subscriber-proportional indicators. The last two indicators implement the so-called pro-rata and user-centric models, which are extensively applied to distribute revenues in the music streaming market. The axioms we propose formalize different principles of fairness, stability, and non-manipulability, and are tailor-made for the streaming context. We complete our analysis with a case study that measures the influence of the 19 most-followed streamers worldwide on the Twitch platform.
Keywords/Subjects:
measure
relevance
proportionality
streaming
axiom
Knowledge area:
CDU: Ciencias puras y naturales: Matemáticas
CDU: Ciencias sociales: Demografía. Sociología. Estadística: Estadística
CDU: Ciencias sociales: Economía
Type of document:
info:eu-repo/semantics/article
Access rights:
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
DOI:
https://doi.org/10.1016/j.jebo.2025.106941
Published in:
Journal of Economic Behavior & Organization
Appears in Collections:
Artículos - Estadística, Matemáticas e Informática



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