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Mining Social Media Data: A Practical Approach with Weka

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  • Pardeep Arora
  • Jass Kaur
  • Anshu

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.

Suggested Citation

  • Pardeep Arora & Jass Kaur & Anshu, 2025. "Mining Social Media Data: A Practical Approach with Weka," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(3), pages 1012-1019, June.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i3:id:914
    DOI: 10.32628/IJSRST25123108
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