Fake News Detection on Kashmir Issue Using Machine Learning Techniques

Abstract: Focusing events are sudden, impactful occurrences that spark widespread discussions. Analyzing fake news during such events is challenging due to limited and short-lived datasets. Online fact checkers are slow in identifying fake news, and internet communities and forums become the primary source of news, allowing unchecked dissemination. This study proposes a machine learning approach to predict fake news during the revocation of Article 370 in Kashmir as a focusing event. Small dataset from 20th August till 2nd September is collected and user profile parameters are utilized for effective classification. Five classifiers were employed, with Random-Forest and Logistic-Regression achieving the highest F1 scores of 74 per cent. Results identifies prevalent words in true and false news tweets, aiding in fake news detection. This approach mitigates misinformation during events with limited data, contributing to a reliable online environment. The research is valuable for major geopolitical shifts, natural disasters, and social movements.

Misbah Kazmi, Sadia Nauman, Sadaf Abdul Rauf, S. Ali, Ali Daud and Bader Alshemaimri. “Fake News Detection on Kashmir Issue Using Machine Learning Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01606106

@article{Kazmi2025,
title = {Fake News Detection on Kashmir Issue Using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01606106},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01606106},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Misbah Kazmi and Sadia Nauman and Sadaf Abdul Rauf and S. Ali and Ali Daud and Bader Alshemaimri}
}