Machine learning and deep learning approaches for fake news detection and related topics in multilingual contexts: a systematic literature review

The rise of fake news has become a critical issue for information integrity, particularly in low-resource languages, which often lack sufficient digital data and tools for machine learning (ML) and deep learning (DL) models. This systematic review examines the effectiveness of ML and DL techniques in detecting fake news across multiple languages, with particular emphasis on the research gap concerning low-resource languages, in contrast to the extensive studies focused on monolingual English. We conducted a comprehensive search across several databases, screening 1,567 records and including 85 studies in our final analysis, based on well-defined inclusion and exclusion criteria. Additionally, the review explores various definitions of fake news and rumors, publicly available datasets, and commonly employed evaluation tools in detection methods. We provide a thorough analysis of both traditional and advanced ML and DL techniques, highlighting key challenges and potential avenues for future research. While these advanced models have led to significant improvements, they are not without limitations. For instance, transformer models, despite their power, may inadvertently capture biases from training data, potentially affecting their performance across different domains or languages. Hybrid models, while enhancing capabilities, may face challenges related to computational costs and scalability. Furthermore, the dependence on large datasets and complex architectures can limit the practicality of these models for fake news detection (FND) in low-resource settings or real-time applications. Consequently, while the integration of advanced models and features has advanced FND, ongoing research is needed to address these challenges and improve model applicability in diverse contexts. Future work should focus on mitigating biases, improving model efficiency, and developing methods to adapt these models for lower-resource environments and real-time scenarios. This study provides a comprehensive roadmap for future research aimed at overcoming these challenges and advancing FND across diverse linguistic contexts.