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.
Highlights
Evolution in network technology and computing power has enabled people to interact with one another over the Internet
We focus on the characteristics of virtual worlds such as massively multiplayer online games (MMOGs), whose rapid changes correlate with increasing or decreasing user populations [34,35,36]
The present paper focuses on EVE Online, one of the largest MMOG virtual worlds, which has attracted approximately 0.5 million subscribers since it was released in May 2003 [37]
Summary
A growing number of users interact with one another and engage in economic, educational, and artistic activities in large virtual worlds (e.g., Second Life). Massively multiplayer online games (MMOGs)—e.g., World of Warcraft and EVE Online—have attracted an increasing number of active users who build communities and participate in a range of interactions [1,2,3,4,5,6]. Accessible data from Pardus, an MMOG, enables researchers to investigate social theories in large virtual populations [7,8,9,10,11,12]. Research on forecasting the value of virtual currencies used in economic activity among virtual world users is currently underway [25, 26]
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