Classification of disinformation in hybrid warfare: an application of XLNet during the Russia’s war against Ukraine

Abstract

The spread of disinformation has become a critical component of hybrid warfare, particularly in Russia’s war against Ukraine, where social media serves as a battlefield for influence and propaganda. This study develops a comprehensive methodology for classifying disinformation in the context of hybrid warfare, focusing on Russia’s war against Ukraine. The objective of this study is to address the challenges of disinformation detection, particularly the increased spread of propaganda due to hybrid warfare. The study focuses on the use of transformer-based language models, specifically, XLNet, to classify multilingual, context-sensitive disinformation. The tasks of this study are to analyze current research and develop a methodology to effectively classify disinformation using the XLNet model. The proposed methodology includes several key components: data preprocessing to ensure quality, application of XLNet for training on diverse datasets, and hyperparameter optimization to handle the complexities of disinformation data. The study used datasets containing pro-Russian and neutral/pro-Ukrainian tweets, and the XLNet model demonstrated strong performance metrics, including high precision, recall, and F1-scores across different dataset sizes. Results showed that accuracy initially improved with increasing data volume but declined slightly with numerous datasets, suggesting the need for balancing data quality and quantity. The proposed methodology addresses the gaps in automated disinformation detection by integrating transformer-based models with advanced preprocessing and training techniques. This research improves the capacity for real-time detection and analysis of disinformation, thus contributing to public information governance and strategic communication efforts during wartime.