Cuckoo Swarm Optimization: An Intelligent Feature Selection Approach for Enhanced Fake News Detection
Authors
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Nikita Garg
Department of Computer Science and Engineering, School of Engineering and Technology, H.N.B. Garhwal University, Srinagar Garhwal, Uttarakhand 246174, India
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Pritam Singh Negi
- Nischay Garg Department of Computer Science and Engineering, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India
DOI:
https://doi.org/10.68163/ics.2026.67Keywords:
Cuckoo search, fake news detection, feature selection, machine learningAbstract
Objective: This study aimed to develop and evaluate a machine learning-based framework for text-based fake news detection by integrating Cuckoo Search (CS) algorithm-based feature selection with conventional machine learning classifiers.
Materials and Methods: Five machine learning classifiers, namely Decision Tree, Support Vector Machine (SVM), XGBoost, Logistic Regression and Random Forest, were evaluated using a CS-based feature selection approach. Model performance was assessed using accuracy, precision, recall and F1-score.
Results: The Random Forest classifier combined with CS-based feature selection demonstrated the highest overall performance, achieving an accuracy of 91%, precision of 90%, recall of 92% and F1-score of 91%. Its performance exceeded that of the other evaluated classifiers under the investigated conditions. In contrast, the Decision Tree classifier exhibited comparatively lower performance, which may be attributable to its greater sensitivity to the structure and characteristics of the selected feature space.
Conclusion: The findings demonstrate that integrating metaheuristic feature selection with conventional machine learning algorithms can provide an effective and computationally manageable approach for text-based fake news detection. In particular, CS-based feature selection combined with Random Forest effectively identified a discriminatory feature subset and improved classification performance, highlighting its potential for automated misinformation detection on digital platforms.
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Copyright (c) 2026 Nikita Garg, Pritam Singh Negi, Nischay Garg
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