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Predicting Virtual World User Population Fluctuations with Deep Learning.

Young Bin Kim1, Nuri Park2, Qimeng Zhang1

  • 1Interdisciplinary Program in Visual Information Processing, Korea University, Seoul, Korea.

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Summary
This summary is machine-generated.

This study introduces a deep learning system to predict virtual world user population changes. By analyzing online data, it offers a novel method for understanding user activity fluctuations in virtual environments.

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Virtual Worlds

Background:

  • Predicting user population dynamics is crucial for virtual world management.
  • Existing methods for forecasting user fluctuations are not well-established.
  • Understanding user behavior is key to the success and sustainability of virtual environments.

Purpose of the Study:

  • To propose and evaluate a novel system for predicting user action increases in virtual worlds.
  • To address the lack of documented methods for forecasting virtual world population changes.
  • To leverage accessible online data for predicting user population trends.

Main Methods:

  • Utilizing deep learning algorithms for predictive modeling.
  • Employing a diverse range of data sources, including Google Trends, Wikipedia, online communities, and forums.
  • Developing a system to analyze and forecast user population fluctuations.

Main Results:

  • The proposed system demonstrates the potential for predicting user population changes.
  • Analysis of EVE Online's user population using the developed system.
  • Validation of the deep learning approach for virtual world analytics.

Conclusions:

  • Deep learning offers a viable approach to predict virtual world user population dynamics.
  • Accessible online data can be effectively utilized for forecasting user activity.
  • The developed system provides a foundation for future research in virtual world population management.