IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-33-6652-7_3.html
   My bibliography  Save this book chapter

Credibility Analysis in Social Big Data

In: Social Big Data Analytics

Author

Listed:
  • Bilal Abu-Salih

    (The University of Jordan)

  • Pornpit Wongthongtham

    (The University of Western Australia)

  • Dengya Zhu

    (Curtin University)

  • Kit Yan Chan

    (Curtin University)

  • Amit Rudra

    (Curtin University)

Abstract

The concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment’s settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also lists an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users’ trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

Suggested Citation

  • Bilal Abu-Salih & Pornpit Wongthongtham & Dengya Zhu & Kit Yan Chan & Amit Rudra, 2021. "Credibility Analysis in Social Big Data," Springer Books, in: Social Big Data Analytics, chapter 0, pages 61-88, Springer.
  • Handle: RePEc:spr:sprchp:978-981-33-6652-7_3
    DOI: 10.1007/978-981-33-6652-7_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-981-33-6652-7_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.