Context-Aware Multilingual Fake News Detection Using Machine Learning and Genetic Algorithm-Based Feature Selection

The rapid proliferation of fake news across multilingual digital platforms poses a significant challenge for information

reliability and societal trust. Existing detection approaches often focus on monolingual datasets or fail to integrate robust feature

selection with context-aware embeddings, limiting their scalability and effectiveness. This study proposes a novel multilingual

fake-news detection framework that combines translation-driven label alignment, dense context-aware embeddings via SentenceBERT (SBERT), and Genetic Algorithm-based feature selection, followed by evaluation using multiple ensemble and traditional

classifiers. The framework is validated on English and Bengali datasets, where Bengali news is translated to English and labels

are generated through cosine similarity with the English dataset. By extracting semantic-rich embeddings and optimizing feature

subsets, the framework effectively reduces dimensionality while retaining discriminative features, enabling enhanced model

performance. Experimental results demonstrate that ensemble models, particularly Gradient Boosting and Random Forest,

consistently achieve superior accuracy and robustness across languages, with the framework outperforming traditional

monolingual and non-optimized approaches. The proposed pipeline addresses the gaps of multilingual alignment, optimizationdriven feature selection, and ensemble evaluation in a unified architecture, offering a scalable, language-independent, and

interpretable solution for fake-news detection. These findings highlight the potential of integrating cross-lingual semantic

understanding and evolutionary optimization for reliable detection of misinformation in diverse linguistic contexts, providing a

foundation for future research in low-resource and multilingual settings