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Predicting Virtual World User Population Fluctuations with Deep Learning

Author

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

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.

Suggested Citation

  • Young Bin Kim & Nuri Park & Qimeng Zhang & Jun Gi Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Virtual World User Population Fluctuations with Deep Learning," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0167153
    DOI: 10.1371/journal.pone.0167153
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    References listed on IDEAS

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    1. Mryglod, O. & Fuchs, B. & Szell, M. & Holovatch, Yu. & Thurner, S., 2015. "Interevent time distributions of human multi-level activity in a virtual world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 681-690.
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    Cited by:

    1. Young Bin Kim & Kyeongpil Kang & Jaegul Choo & Shin Jin Kang & TaeHyeong Kim & JaeHo Im & Jong-Hyun Kim & Chang Hun Kim, 2017. "Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    2. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.

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