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Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum

Author

Listed:
  • Young Bin Kim
  • Kyeongpil Kang
  • Jaegul Choo
  • Shin Jin Kang
  • TaeHyeong Kim
  • JaeHo Im
  • Jong-Hyun Kim
  • Chang Hun Kim

Abstract

Transactions involving virtual currencies are becoming increasingly common, including those in online games. In response, predicting the market price of a virtual currency is an important task for all involved, but it has not yet attracted much attention from researchers. This paper presents user opinions from online forums in a massive multiplayer online game (MMOG) setting widely used around the world. We propose a method for predicting the next-day rise and fall of the currency used in an MMOG environment. Based on analysis of online forum users’ opinions, we predict daily fluctuations in the price of a currency used in an MMOG setting. Focusing specifically on the World of Warcraft game, one of the most widely used MMOGs, we demonstrate the feasibility of predicting the fluctuation in value of virtual currencies used in this game community.

Suggested Citation

  • Young Bin Kim & Kyeongpil Kang & Jaegul Choo & Shin Jin Kang & TaeHyeong Kim & JaeHo Im & Jong-Hyun Kim & Chang Hun Kim, 2017. "Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    • Handle: RePEc:hin:complx:4152705
    DOI: 10.1155/2017/4152705
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    References listed on IDEAS

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    1. Stefan Thurner & Michael Szell & Roberta Sinatra, 2012. "Emergence of Good Conduct, Scaling and Zipf Laws in Human Behavioral Sequences in an Online World," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-7, January.
    2. Young Bin Kim & Nuri Park & Qimeng Zhang & Jun Gi Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Virtual World User Population Fluctuations with Deep Learning," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.
    3. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    4. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    5. Benedikt Fuchs & Stefan Thurner, 2014. "Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-13, August.
    6. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    7. Mryglod, O. & Fuchs, B. & Szell, M. & Holovatch, Yu. & Thurner, S., 2015. "Interevent time distributions of human multi-level activity in a virtual world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 681-690.
    8. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    9. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    10. Young Bin Kim & Jun Gi Kim & Wook Kim & Jae Ho Im & Tae Hyeong Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
    11. Yochi Cohen-Charash & Charles A Scherbaum & John D Kammeyer-Mueller & Barry M Staw, 2013. "Mood and the Market: Can Press Reports of Investors' Mood Predict Stock Prices?," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
    12. Michael Szell & Stefan Thurner, 2012. "Social Dynamics In A Large-Scale Online Game," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-18.
    13. Pietro Panzarasa & Tore Opsahl & Kathleen M. Carley, 2009. "Patterns and dynamics of users' behavior and interaction: Network analysis of an online community," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 911-932, May.
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