IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v13y2024i1p372-380.html
   My bibliography  Save this article

Improving the prediction of social media engagement in universities by utilizing feature selection in machine learning

Author

Listed:
  • Dino Keco

    (Faculty of Engineering, Natural and Medical Sciences, Department of Information Technology, International Burch University, Francuske revolucije bb., 71000 Sarajevo, Bosnia and Herzegovina)

  • Engin Obucic

    (Faculty of Economics and Social Sciences, Department of Management, International Burch University, Francuske revolucije bb., 71000 Sarajevo, Bosnia and Herzegovina)

  • Mersid Poturak

    (Faculty of Economics and Social Sciences, Department of Management, International Burch University, Francuske revolucije bb., 71000 Sarajevo, Bosnia and Herzegovina)

Abstract

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions. Key Words:Machine learning, social media, Facebook, feature selection, user engagement

Suggested Citation

  • Dino Keco & Engin Obucic & Mersid Poturak, 2024. "Improving the prediction of social media engagement in universities by utilizing feature selection in machine learning," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 13(1), pages 372-380, January.
    • Handle: RePEc:rbs:ijbrss:v:13:y:2024:i:1:p:372-380
    DOI: 10.20525/ijrbs.v13i1.3132
    as

    Download full text from publisher

    File URL: https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/3132/2208
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v13i1.3132
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v13i1.3132?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
    ---><---

    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:rbs:ijbrss:v:13:y:2024:i:1:p:372-380. 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: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    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.