Journal Article

Enhancing Fake News Detection via PSO-Optimized Ensemble Learning: A Comparative Study of SVM, NB, and RF

Kadum, Amaal, Abdalrdha, Zainab Khyioon, Naser, Wedad Abdul Khuder, Kadhim, Amal Abbas

2025

Overview

Given the rapid spread of fake news across digital platforms, there is a pressing need for a reliable and efficient detection method. Current ensemble learning models often lack optimal weight tuning, limiting their performance in fake news classification tasks. To address this gap, we propose a Particle Swarm Optimization (PSO)-optimized ensemble model that integrates Support Vector Machines (SVM), Naive Bayes, and Random Forest (RF) classifiers using a soft voting strategy. Text data is preprocessed and transformed into numerical features using TF-IDF vectorization. The dataset, derived from the ISOT Fake News corpus, is split into training (80%) and testing (20%) subsets. Each base classifier is individually trained and evaluated, followed by the construction and assessment of an unoptimized voting ensemble. Subsequently, PSO is employed to fine-tune the weights of the base classifiers within the voting ensemble, enhancing overall prediction performance. The optimized model achieves 98.32% accuracy and an F1-score of 98.33%, outperforming both the unoptimized ensemble and standalone classifiers, as well as surpassing several state-of-the-art methods. This approach not only improves detection accuracy but also offers a scalable, interpretable, and effective solution to the fake news problem. Performance is evaluated using standard metrics such as ROC curves and confusion matrices, providing a comprehensive assessment of the model’s reliability.

• Publisher: International Information and Engineering Technology Association (IIETA)
• Subject: Accuracy / Algorithms / Classification / Comparative studies / Datasets / Ensemble learning / False information / Literature reviews / Machine learning / News / Particle swarm optimization / Performance evaluation / Public health / Reliability / Sentiment analysis / Social networks / Support vector machines