Advertisement

Dynamic Evolution of Social Network in OHCs Based on Stochastic Actor-Based Model: A Case Study of WeChat Group

  • Chengkun Wang
  • Jingxuan Cai
  • Jiahui Gao
  • Jiang WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11924)

Abstract

With the seamless integration of Internet technology and medical industry, the online health community (OHC), transcending the restrictions of time and geo-graphical distances, provides users with rich and customized information services as well as emotional support. By studying user behavior and the social network evolution in the online health community, it can effectively improve the user activity of the online health community and the efficiency of users’ access to information. In this paper, the online health WeChat group is taken as the research object to established a social network based on the @ relationship among users. It explores the influence of individual behaviors on the evolution of network structure through the Stochastic Actor Model. Results show that the user behaviors in the online health WeChat group are of periodicity and turnover. The frequency of speech works differently for the overall and individual network structure changes. It promotes the enrichment of overall network structure while impede the @ relationships among individuals. This paper, exploring the network structure evolution in online health community, carries guiding suggestions concerning the management and maintenance of the online health community.

Keywords

Social network analysis WeChat group Online health community Net evolution 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 71573197).

References

  1. 1.
    Martijn, V.D.E., Faber, M.J., Aarts, J.W.M., Kremer, J.A.M., Marten, M., Bloem, B.R.: Using online health communities to deliver patient-centered care to people with chronic conditions. J. Med. Internet Res. 15(6), e115 (2013)CrossRefGoogle Scholar
  2. 2.
    Qiu, J., Li, Y., Jie, T., Zheng, L., Hopcroft, J.E.: The lifecycle and cascade of WeChat social messaging groups. In: International Conference on World Wide Web (2016)Google Scholar
  3. 3.
    Macias, W., Lewis, L.S., Smith, T.L.: Health-related message boards/chat rooms on the Web: discussion content and implications for pharmaceutical sponsorships. J Health Commun. 10(3), 209–223 (2005)CrossRefGoogle Scholar
  4. 4.
    Wang, X., Zhao, K., Street, N.: Social support and user engagement in online health communities. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds.) ICSH 2014. LNCS, vol. 8549, pp. 97–110. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08416-9_10CrossRefGoogle Scholar
  5. 5.
    Che, H.L., Cao, Y.: Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: evidence from China. Comput. Hum. Behav. 41, 104–111 (2014)CrossRefGoogle Scholar
  6. 6.
    Gan, C.: Understanding WeChat users’ liking behavior: an empirical study in China. Comput. Hum. Behav. 68, 30–39 (2017)CrossRefGoogle Scholar
  7. 7.
    Bambina, A.: Online Social Support: The Interplay of Social Networks and Computer-Mediated Communication. Cambria Press, Youngstown (2007)Google Scholar
  8. 8.
    Snijders, T.A.B., Steglich, C.E.G., Schweinberger, M.: Manual for SIENA version 3. Times Literary Supplement TLS, vol. 14, no. 2, pp. 257–258 (2005)Google Scholar
  9. 9.
    Snijders, T.A.B., Bunt, G.G.V.D., Steglich, C.E.G.: Introduction to stochastic actor-based for network dynamics. Soc. Netw. 32(1), 44–60 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chengkun Wang
    • 1
  • Jingxuan Cai
    • 1
  • Jiahui Gao
    • 1
  • Jiang Wu
    • 1
    Email author
  1. 1.School of Information Management, Center for E-Commerce Research and DevelopmentWuhan UniversityWuhanChina

Personalised recommendations