IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v133y2018icp220-228.html
   My bibliography  Save this article

Identifying the features of reputable users in eWOM communities by using Particle Swarm Optimization

Author

Listed:
  • Martínez-Torres, M.R.
  • Arenas-Marquez, F.J.
  • Olmedilla, M.
  • Toral, S.L.

Abstract

Electronic Word-of-Mouth communities have become popular over the last several years as websites where people can share their online reviews about any type of product or service. As a mechanism to improve trust, posted reviews can also be scored by the rest of the community in terms of helpfulness, so users can reach a high level of reputation through their interactions with other users and can thus increase their credibility. The aim of this paper is to investigate the main patterns of activity that characterize reputable users by using a set of classification rules. However, the class of reputable users is only a fraction of the total number of users. Due to the imbalance between the classes, i.e., reputable and non-reputable users, and the high dimensionality of the problem, an evolutionary computation algorithm such as Particle Swarm Optimization (PSO) is applied to obtain the main activity patterns of reputable users. Obtained results can help us better understand the mechanism of trust in eWOM communities and avoid the undesirable manipulation of reputations by false accounts.

Suggested Citation

  • Martínez-Torres, M.R. & Arenas-Marquez, F.J. & Olmedilla, M. & Toral, S.L., 2018. "Identifying the features of reputable users in eWOM communities by using Particle Swarm Optimization," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 220-228.
    • Handle: RePEc:eee:tefoso:v:133:y:2018:i:c:p:220-228
    DOI: 10.1016/j.techfore.2018.04.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162516306783
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2018.04.017?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.

    Citations

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


    Cited by:

    1. Foroudi, Pantea & Yu, Qionglei & Gupta, Suraksha & Foroudi, Mohammad M., 2019. "Enhancing university brand image and reputation through customer value co-creation behaviour," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 218-227.
    2. Bigne, Enrique & Ruiz, Carla & Curras-Perez, Rafael, 2024. "How consumers process online review types in familiar versus unfamiliar destinations. A self-reported and neuroscientific study," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    3. Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

    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:tefoso:v:133:y:2018:i:c:p:220-228. 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: http://www.sciencedirect.com/science/journal/00401625 .

    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.