Predicting Virtual World User Population Fluctuations with Deep Learning

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書誌詳細
出版年:	PLoS One vol. 11, no. 12 (Dec 2016), p. e0167153
第一著者:	Young Bin Kim
その他の著者:	Park, Nuri, Zhang, Qimeng, Kim, Jun Gi, Shin Jin Kang, Kim, Chang Hun
出版事項:	Public Library of Science
主題:	Korea University Wikipedia Radio Communications Economic Research Radio communications International conferences Population Computer & video games Internet Trends Signal processing Interdisciplinary aspects Researchers Virtual communities Community Fluctuations Use statistics Populations Studies Information processing Data collection Acoustics Economic activity Deep learning Datasets
オンライン･アクセス:	Citation/Abstract Full Text Full Text - PDF
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その他の書誌記述
抄録:	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.
ISSN:	1932-6203
DOI:	10.1371/journal.pone.0167153
ソース:	Health & Medical Collection