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Predicting Virtual World User Population Fluctuations with Deep Learning

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Publié dans:PLoS One vol. 11, no. 12 (Dec 2016), p. e0167153
Auteur principal: Young Bin Kim
Autres auteurs: Park, Nuri, Zhang, Qimeng, Kim, Jun Gi, Shin Jin Kang, Kim, Chang Hun
Publié:
Public Library of Science
Sujets:
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
Accès en ligne:Citation/Abstract
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Résumé: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
Source:Health & Medical Collection