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

Dino Keco, Engin Obucic and Mersid Poturak
Additional contact information
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

International Journal of Research in Business and Social Science (2147-4478), 2024, vol. 13, issue 1, 372-380

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

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/3132/2208 (application/pdf)
https://doi.org/10.20525/ijrbs.v13i1.3132 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rbs:ijbrss:v:13:y:2024:i:1:p:372-380

Access Statistics for this article

International Journal of Research in Business and Social Science (2147-4478) is currently edited by Prof.Dr.Umit Hacioglu

More articles in International Journal of Research in Business and Social Science (2147-4478) from Center for the Strategic Studies in Business and Finance Editorial Office,Baris Mah. Enver Adakan Cd. No: 5/8, Beylikduzu, Istanbul, Turkey. Contact information at EDIRC.
Bibliographic data for series maintained by Umit Hacioglu ().