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A short-term trend prediction model of topic over Sina Weibo dataset

Author

Listed:
  • Juanjuan Zhao

    (Taiyuan University of Technology)

  • Weili Wu

    (University of Texas at Dallas)

  • Xiaolong Zhang

    (Pennsylvania State University)

  • Yan Qiang

    (Taiyuan University of Technology)

  • Tao Liu

    (Taiyuan University of Technology)

  • Lidong Wu

    (University of Texas at Dallas)

Abstract

Microblog has become a popular social network service. It provides a new communication platform for information acquisition, sharing and spreading. In addition to presenting daily-life reports from users, microblog also reports unexpected events, which get broad attention. How to forecast such unexpected events as early as possible? In this paper, we propose a short-term trend prediction model of topics in Sina Weibo, the most popular microblog service in China. Based on real microblog data, we first analyze which Weibo data attributes have influence on the spreading of topics, and then build a topic spreading model. Further, we develop a model of short-term trend prediction of topics. With dataset from Weibo, we test our algorithm and analyze the experimental data which shows that the proposed model can give a short-term trend prediction of Weibo topic.

Suggested Citation

  • Juanjuan Zhao & Weili Wu & Xiaolong Zhang & Yan Qiang & Tao Liu & Lidong Wu, 2014. "A short-term trend prediction model of topic over Sina Weibo dataset," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 613-625, October.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-013-9674-0
    DOI: 10.1007/s10878-013-9674-0
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    References listed on IDEAS

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    1. Si, Xia-Meng & Liu, Yun & Xiong, Fei & Zhang, Yan-Chao & Ding, Fei & Cheng, Hui, 2010. "Effects of selective attention on continuous opinions and discrete decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3711-3719.
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    Cited by:

    1. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Hengmin Zhu & Li Qian & Wang Qin & Jing Wei & Chao Shen, 2022. "Evolution analysis of online topics based on ‘word-topic’ coupling network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3767-3792, July.

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