LLM-based Multilingual Fake News Detection using Cross-Lingual Semantic Alignment and Contextual Reasoning

Abstract

The production of misinformation on digital platforms has been a concern in recent years, especially in interlingual contexts – people who create content in different languages and language elements that are integrated into written content.the existing fake news detectors use mostly only one language, and they are not adequately cross-linguistically generalizable in using the valuable detectors especially with low-resource languages. To overcome these weaknesses, the present paper suggests an LLM-based multilingual fake news detector which combines cross-lingual semantic alignment and contextual reasoning. The proposed approach involves the fine-tuned multilingual language model, which involves a transformer to derive contextual embeddings of multilingual text. The semantic alignment process (contrastive) is used to project the representation of the various languages to a common embedding space, and hence the transfer of knowledge. Moreover, semantic inconsistencies and misguiding tendency contribute to the reasoning of the model deducing abilities applied in the presence of attention processes. Experiments with the mixed English, Hindi, and Tamil data reveal that the proposed model leads to the accuracy of 94.3 and the F1-score of 93.6, which is better than a baseline model, including SVM, LSTM, and mBERT. The results enable mentioning the great overallization possibility and low-resource, multilingual applicability of the model. The paper gives a scaled and efficient approach to tackling the real-life aspect of multilingual fake news detection.