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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.

Details

1009240
Subject
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
Business indexing term
Company / organization
Name:
Korea University
NAICS:
611310;
Name:
Wikipedia
NAICS:
513140, 516210;
Name:
Radio Communications
NAICS:
517312
Identifier / keyword
Internet; User; Virtual world; Deep learning; Forecasting; Online encyclopedias; Population dynamics; Social media; Social theory; Algorithms; Human learning
Title
Predicting Virtual World User Population Fluctuations with Deep Learning
Author
Young Bin Kim; Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Shin Jin Kang; Kim, Chang Hun
Publication title
PLoS One; San Francisco
Volume
11
Issue
12
First page
e0167153
Publication year
2016
Publication date
Dec 2016
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
Publication subject
Medical Sciences, Sciences: Comprehensive Works
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
DOI
https://doi.org/10.1371/journal.pone.0167153
ProQuest document ID
1847562984
Document URL
https://www.proquest.com/scholarly-journals/predicting-virtual-world-user-population/docview/1847562984/se-2?accountid=208611
Copyright
© 2016 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-10-03
Database
ProQuest One Academic