Predicting virtual world user population fluctuations with deep learning

Young Bin Kim, Nuri Park, Qimeng Zhang, Jun Gi Kim, Shin Jin Kang, Chang-Hun Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

Original languageEnglish
Article numbere0167153
JournalPLoS One
Volume11
Issue number12
DOIs
Publication statusPublished - 2016 Dec 1

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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