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

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.

Keywords: {'name': 'Machine learning, social media, Facebook, feature selection, user engagement'}

Authors & affiliations

Dino Keco
Affiliation not supplied

Engin Obucic
Affiliation not supplied

Mersid Poturak
Affiliation not supplied

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