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Predicting Retweeting Behavior Based on BPNN in Emergency Incidents

Author

Listed:
  • Xuejun Ding

    (School of Management, Hefei University of Technology, Hefei, P. R. China2School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, P. R. China)

  • Yong Tian

    (School of Physics and Electronic Technology, Liaoning Normal University, Dalian 116029, P. R. China)

Abstract

Emergency incidents can trigger heated discussions on microblogging platforms, and a great number of tweets related to emergency incidents are retweeted by users. Consequently, social media big data related to the emergency incidents is generated from various social media platforms, which can be used to predict users’ retweeting behavior. In this paper, the characteristics of individuals’ retweeting behaviors in emergency incidents are analyzed, and then 11 important characteristics are extracted from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage. A back propagation neural network (BPNN) model called PRBBP is used to predict retweeting behavior in such emergency incidents. Based on PRBBP, an algorithm called PRABP is proposed to predict the number of retweets in emergency incidents. The experiments are performed on a large-scale dataset crawled from Sina weibo. The simulation results show that both the PRBBP model and the PRABP algorithm proposed by this paper have excellent predictive performance.

Suggested Citation

  • Xuejun Ding & Yong Tian, 2017. "Predicting Retweeting Behavior Based on BPNN in Emergency Incidents," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-17, February.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:01:n:s0217595917400115
    DOI: 10.1142/S0217595917400115
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