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Depression Detection in Social Media Data for Resource-Constrained Roman Urdu Using Machine Learning Methods

Authors

  • Naveed Kaimkhani

    Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
    Author

  • Memoona Sami

    Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
    Author

  • Amirita Dewani

    Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
    Author

  • Junaid Ahmed

    Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
    Author

  • Mariam Jawed

    Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.0704

Keywords:

Depression detection, machine learning, Roman Urdu, Classifiers, Mental Health

Abstract

nxiety and depression are crucial issues related to human mental health globally and are especially prevalent among users of social networking and online platforms such as X and Reddit. Roman Urdu has become a dominant language for expressing and communicating personal feelings on these platforms, making it a valuable resource for detecting early signs of depression. This study focuses on the automatic detection of depression in Roman Urdu social media posts using machine learning methods. A human-annotated corpus of 6,000 posts was created, and four machine learning classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), were trained using natural language processing features such as TF-IDF. The accuracy, training time, and testing time of these models were compared. The results demonstrate that the Random Forest classifier outperforms the other models, achieving 93% accuracy with high efficiency. The study highlights the effectiveness of scalable machine learning-based early detection methods for underrepresented languages such as Roman Urdu.

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References

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Published

2026-07-01

Issue

Vol. 45 No. 3 (2026): July Issue

Section

Articles

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Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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How to Cite

Depression Detection in Social Media Data for Resource-Constrained Roman Urdu Using Machine Learning Methods. (2026). Mehran University Research Journal of Engineering and Technology, 45(3), 143-151. https://doi.org/10.22581/muet1982.0704

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