Mining Social Media Data: A Practical Approach with Weka

Pardeep Arora, Jass Kaur and Anshu

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 1012-1019

Abstract: Due to the massive amount of data produced by social media's explosive growth, analysis and insight extraction are becoming more difficult. This study focuses on machine learning classification problems on various social media datasets. The datasets include "Time-Waster on Social Media," "Instagram Profile," "Instagram Photos," along with "Viral Trends." The Bayes Network, Random Forest, Decision Tree (J48), and Naive Bayes algorithms were employed. On the "Time-Waster" and "Instagram Photos" datasets, Random Forest outperformed the others. Machine learning algorithms like F-Measure, Precision, Recall, and ROC AUC were employed in the evaluation. Multimedia content may be investigated in future research to gain a greater understanding of user trends and behavior.

Keywords: Data Mining; Social Media; Analysis; Data sets; Machine learning algorithms (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:914

DOI: 10.32628/IJSRST25123108

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