IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v51y2020ics0268401218313057.html
   My bibliography  Save this article

Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage

Author

Listed:
  • Gloor, Peter
  • Fronzetti Colladon, Andrea
  • de Oliveira, Joao Marcos
  • Rovelli, Paola

Abstract

Internet and social media offer firms novel ways of managing their marketing strategy and gain competitive advantage. The groups of users expressing themselves on the Internet about a particular topic, product, or brand are frequently called a virtual tribe or E-tribe. However, there are no automatic tools for identifying and studying the characteristics of these virtual tribes. Towards this aim, this paper presents Tribefinder, a system to reveal Twitter users’ tribal affiliations, by analyzing their tweets and language use. To show the potential of this instrument, we provide an example considering three specific tribal macro-categories: alternative realities, lifestyle, and recreation. In addition, we discuss the different characteristics of each identified tribe, in terms of use of language and social interaction metrics. Tribefinder illustrates the importance of adopting a new lens for studying virtual tribes, which is crucial for firms to properly design their marketing strategy, and for scholars to extend prior marketing research.

Suggested Citation

  • Gloor, Peter & Fronzetti Colladon, Andrea & de Oliveira, Joao Marcos & Rovelli, Paola, 2020. "Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage," International Journal of Information Management, Elsevier, vol. 51(C).
  • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401218313057
    DOI: 10.1016/j.ijinfomgt.2019.03.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401218313057
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijinfomgt.2019.03.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:ininma:v:51:y:2020:i:c:s0268401218313057. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    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.