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Structural diversity effect on hashtag adoption in Twitter

Author

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  • Zhang, Aihua
  • Zheng, Mingxing
  • Pang, Bowen

Abstract

With online social network developing rapidly these years, user’ behavior in online social network has attracted a lot of attentions to it. In this paper, we study Twitter user’s behavior of hashtag adoption from the perspective of social contagion and focus on “structure diversity” effect on individual’s behavior in Twitter. We achieve data through Twitter’s API by crawling and build a users’ network to carry on empirical research. The Girvan–Newman (G–N) algorithm is used to analyze the structural diversity of user’s ego network, and Logistic regression model is adopted to examine the hypothesis. The findings of our empirical study indicate that user’ behavior in online social network is indeed influenced by his friends and his decision is significantly affected by the number of groups that these friends belong to, which we call structural diversity.

Suggested Citation

  • Zhang, Aihua & Zheng, Mingxing & Pang, Bowen, 2018. "Structural diversity effect on hashtag adoption in Twitter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 267-275.
    • Handle: RePEc:eee:phsmap:v:493:y:2018:i:c:p:267-275
    DOI: 10.1016/j.physa.2017.09.075
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    Cited by:

    1. Natalia Sánchez-Arrieta & Rafael A. González & Antonio Cañabate & Ferran Sabate, 2021. "Social Capital on Social Networking Sites: A Social Network Perspective," Sustainability, MDPI, vol. 13(9), pages 1-35, May.
    2. Jiang, Yubo & Du, Xin & Jin, Tao, 2019. "Using combined network information to predict mobile application usage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 430-439.

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