IDEAS home Printed from https://ideas.repec.org/a/igg/jiit00/v9y2013i1p1-20.html
   My bibliography  Save this article

Identifying Influencers in Online Social Networks: The Role of Tie Strength

Author

Listed:
  • Yifeng Zhang

    (Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA)

  • Xiaoqing Li

    (Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA)

  • Te-Wei Wang

    (Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA)

Abstract

Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

Suggested Citation

  • Yifeng Zhang & Xiaoqing Li & Te-Wei Wang, 2013. "Identifying Influencers in Online Social Networks: The Role of Tie Strength," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 9(1), pages 1-20, January.
    • Handle: RePEc:igg:jiit00:v:9:y:2013:i:1:p:1-20
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jiit.2013010101
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kulkarni, Kalpak K. & Kalro, Arti D. & Sharma, Dinesh & Sharma, Piyush, 2020. "A typology of viral ad sharers using sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jiit00:v:9:y:2013:i:1:p:1-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.