Measures of relevance to the success of streaming platforms

TL;DR

An axiomatic analysis is performed to provide normative foundations for three relevance metrics: the uniform, the proportional, and the subscriber-proportional indicators and proposes different principles of fairness, stability, and non-manipulability, tailor-made for the streaming context.

Abstract

Digital streaming platforms, including Twitch, Spotify, Netflix, Disney, and Kindle, have emerged as one of the main 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 is information that can play a key role in revenue allocation. We perform an axiomatic analysis to provide normative foundations for three relevance metrics: the 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.

Figures (2)

• Figure 1: Levels of information on a platform.
• Figure 2: Levels of information and properties.

Theorems & Definitions (25)

• Example 1
• Theorem 1
• proof
• Remark 1
• Theorem 2
• proof
• Remark 2
• Theorem 3
• proof
• Remark 3