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

Department of Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

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 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

ASJC Scopus subject areas

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

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title = "Predicting virtual world user population fluctuations with deep learning",

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.",

author = "Kim, {Young Bin} and Nuri Park and Qimeng Zhang and Kim, {Jun Gi} and Kang, {Shin Jin} and Kim, {Chang Hun}",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, ICT and future Planning (NRF-2014R1A2A2A01007143, NRF- 2015R1A2A1A16074940, and NRF- 2015R1A1A1A05001196) and the ICT R&D program of MSIP/IITP (R-20160404-003511, High performance computing (HPC) based rendering solution development). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",

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AU - Kang, Shin Jin

AU - Kim, Chang Hun

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, ICT and future Planning (NRF-2014R1A2A2A01007143, NRF- 2015R1A2A1A16074940, and NRF- 2015R1A1A1A05001196) and the ICT R&D program of MSIP/IITP (R-20160404-003511, High performance computing (HPC) based rendering solution development). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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