A sophisticated feature vectorization-based stacked machine learning approach for fake news detection in Bangla and English

Abstract

The rapid spread of fake news through social media and the internet poses a major challenge, especially in developing countries like Bangladesh. Fake news, consisting of misleading or fabricated content, can severely impact individuals, organizations, and society. Many existing detection methods are complex and resource-intensive, making them unsuitable for real-time applications or low-resource languages. To address this issue, we proposed an efficient fake news detection system using a stacked machine learning model with TF-IDF for feature extraction. TF-IDF converts text into a numerical representation by emphasizing key terms, while the stacked ensemble model improves classification accuracy by integrating multiple machine learning algorithms. We also explored various machine learning classifiers, as well as the mBERT and Word2Vec feature vectorization techniques. We evaluated our system using three datasets: one English and two Bangla fake news detection datasets. The TF-IDF + Stacking model achieved 99.6% accuracy and a 99.8% F1-score on the English dataset. For the first Bangla dataset, accuracy improved from 74.8 to 85.2% after applying SMOTE. On the second Bangla dataset, BanFakeNews, the model achieved 98.4% accuracy, demonstrating strong performance across languages. This research introduces a lightweight yet highly effective machine learning-based fake news detection system, making it suitable for real-world applications, especially in multilingual and resource-limited settings.