Exponential random graph models: explaining strategic patterns of collaboration between artists in the music industry with data from Spotify

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

Given the abundance of online data, the exponential random graph models are helpful tools that enable researchers to answer causal inference questions considering cross-sectional network data. This chapter explains (a) what an ERGM is, (b) what kind of research this class of model can help with, (c) how to conduct the analysis, and (d) the type of conclusions that can be drawn. A case study focusing on patterns of collaborations between Rock musicians employing network data from Spotify is presented to show evidence about how to conduct this type of research. The empirical results from the analysis of the Spotify sample provide statistical evidence for the pattern of collaboration between Rock musicians. Some musicians decide not to collaborate; others collaborate only with another musician or band. Besides, musicians who employ the strategy to have more than one collaborator produce music with musicians or bands who already work with their collaborators exploiting the existing network of relationships. This chapter should provide ideas and prompt relevant research in the Social Computing field.
Original languageEnglish
Title of host publicationHandbook of Social Computing
EditorsPeter A. Gloor, Francesca Grippa, Andrea Fronzetti Colladon, Aleksandra Przegalinska
PublisherEdward Elgar Publishing Ltd.
Chapter2
Pages12-26
Number of pages14
Editionfirst
ISBN (Electronic)9781803921259
ISBN (Print)9781803921242
DOIs
Publication statusPublished - 19 Mar 2024

Publication series

NameBusiness

Keywords

  • ERGMs
  • Causal inference
  • Spotify data
  • Entrepreneurial behavior of musicians
  • Collaboration networks
  • Online data

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