Machine Learning Methods for Detecting Fake News: A Systematic Literature Review of Machine Learning Applications in Key Domains

Rapid digitisation in communication and online platform growth have transformed information dissemination and facilitated rapid access while simultaneously amplifying the spread of fake news. This widespread issue undermines public trust, destabilises political systems, and threatens economic stability. Machine learning techniques have been widely applied to fake news detection, but comparative analyses across specific domains such as health, politics, and economics remain limited. Existing reviews tend to focus on supervised learning methods, frequently excluding unsupervised and hybrid approaches, along with unique challenges and dataset requirements of each domain. This study conducted a systematic literature review of machine learning applications for detecting fake news across the three domains. The methodologies and metrics used were evaluated, while key challenges and opportunities were explored. The results revealed a strong reliance on supervised learning techniques, particularly in health-related contexts, where misinformation presented significant risks to public health outcomes. Deep learning methods were promising for processing complex data. Nonetheless, hybrid and unsupervised approaches were underexplored, which presented opportunities to address data scarcity and adaptability. Most datasets originated from social media platforms and news outlets. The common evaluation metrics included accuracy, but advanced measures were rarely applied, which indicated the possibility of enhancing such methods. Persistent challenges include poor data quality, bias, and ethical concerns highlighted the necessity for bias-mitigating algorithms and improved model interpretability. Specifically, economic misinformation has received less attention despite its potential to cause large-scale financial disruptions. This study highlighted that more effective, ethical, and context-specific machine learning solutions are needed to address fake news and enhance digital information credibility.