Knowledge Graphs and Machine Learning in Fake News and Disinformation Detection

The rise of digital platforms has facilitated the rapid spread of disinformation, which poses significant social, political, and economic challenges. Knowledge graphs (KGs) are emerging as effective tools for enhancing the accuracy, interpretability, and scalability of fake news detection systems, addressing limitations in traditional machine learning-based approaches that rely pri-marily on linguistic analysis. This work contains a literature review that synthesizes findings from recent studies on the application of KGs in disinformation detection. We identify how KGs improve detection by encoding real relationships, analyzing context, and enhancing model interpretability, while also discussing current limitations in scalability, data completeness, and contextual adaptability. The reviewed studies underscore the need for future research focusing on scalable, real-time, and cross-linguistic KG models to bolster disinformation detection capabilities globally. Moreover, we present preliminary results of two use cases, showcasing a methodology for constructing KGs that can serve as useful tools to fight against disinformation spread.