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 | |
| Publication status | Published - 2016 Dec 1 |
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ASJC Scopus subject areas
- Medicine(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
Predicting virtual world user population fluctuations with deep learning. / Kim, Young Bin; Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang-Hun.
In: PLoS One, Vol. 11, No. 12, e0167153, 01.12.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predicting virtual world user population fluctuations with deep learning
AU - Kim, Young Bin
AU - Park, Nuri
AU - Zhang, Qimeng
AU - Kim, Jun Gi
AU - Kang, Shin Jin
AU - Kim, Chang-Hun
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85006054913&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006054913&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0167153
DO - 10.1371/journal.pone.0167153
M3 - Article
VL - 11
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 12
M1 - e0167153
ER -