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

doi:10.1371/journal.pone.0167153

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 journal › Article › peer-review

4 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 language	English
Article number	e0167153
Journal	PloS one
Volume	11
Issue number	12
DOIs	https://doi.org/10.1371/journal.pone.0167153
Publication status	Published - 2016 Dec

Bibliographical note

Publisher Copyright:
© 2016 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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AU - Kim, Jun Gi

AU - Kang, Shin Jin

AU - Kim, Chang Hun

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AB - 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.

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Predicting virtual world user population fluctuations with deep learning

Abstract

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