@article{dcd5dda0946246fb8882b79061b06d97,
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. Publisher Copyright: {\textcopyright} 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.",
year = "2016",
month = dec,
doi = "10.1371/journal.pone.0167153",
language = "English",
volume = "11",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "12",
}