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Model for Twitter dynamics: Public attention and time series of tweeting

Author

Listed:
  • Ko, J.
  • Kwon, H.W.
  • Kim, H.S.
  • Lee, K.
  • Choi, M.Y.

Abstract

We present a simple mathematical model for the Twitter dynamics, and use the model to extract the information-sharing tendencies on two time scales, day and hour, about three contenders in the 2012 presidential election in South Korea. Comparison of the model results with actual data demonstrates that the information-sharing tendency on the day scale provides a good measure for the general public attention to the contenders, whereas the tendency on the hour scale reflects the daily cycle of twitter users. In addition, it is attempted to reproduce the time evolution of tweeting by taking the numbers of the articles on the online newspapers as the external driving to tweet, the validity of which is discussed.

Suggested Citation

  • Ko, J. & Kwon, H.W. & Kim, H.S. & Lee, K. & Choi, M.Y., 2014. "Model for Twitter dynamics: Public attention and time series of tweeting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 142-149.
    • Handle: RePEc:eee:phsmap:v:404:y:2014:i:c:p:142-149
    DOI: 10.1016/j.physa.2014.02.034
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
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    Cited by:

    1. Yao, Weiyi & Jiao, Pengfei & Wang, Wenjun & Sun, Yueheng, 2019. "Understanding human reposting patterns on Sina Weibo from a global perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 374-383.
    2. Skaza, Jonathan & Blais, Brian, 2017. "Modeling the infectiousness of Twitter hashtags," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 289-296.
    3. Georgios Magkonis & Karen Jackson, 2019. "Identifying Networks in Social Media: The case of #Grexit," Networks and Spatial Economics, Springer, vol. 19(1), pages 319-330, March.

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