Multilingual Fake News Detection Using Machine Learning with Contextual-Based Feature Extraction

Abstract

Fake news has become a major challenge in today’s digital environment, particularly in languages where labeled data is limited. Most existing research has primarily focused on English due to the easy availability of annotated datasets, whereas low-resource languages such as Bengali remain underexplored. This study presents a multilingual approach for fake news detection using machine learning with contextual-based feature extraction. The proposed method integrates n-gram techniques with sentence-level contextual embeddings to capture both word-level patterns and semantic meaning. Since labeled data is not available for the Bengali dataset, a translation-based strategy is employed, followed by a pseudo-labeling process to assign labels automatically. The models are trained on English news titles and subsequently evaluated on both English and Bengali datasets to examine their cross-lingual effectiveness. The experimental findings indicate that ensemble-based classifiers such as Random Forest and Gradient Boosting achieve reliable performance across both languages. In some cases, the results for Bengali data are comparable or slightly better than those for English. The study demonstrates that effective fake news detection is possible in low-resource languages using short text data without relying on manually labeled datasets. The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments.