Tracking the narrative: A data-driven analysis of media coverage of Russia and Ukraine 2013–2024

This study addresses the lack of global, long-term analyses of media narratives on Russia and Ukraine by examining their evolution from 2013 to 2024. Based on 22.2 million article titles from 14,622 sources operating in 194 countries and covering up to 74 languages, this study employs a hybrid human-AI approach combining multilingual clustering, cluster linking, manual annotation, entity analysis, and analysis of the language distribution of articles by Russian sources. We show that media coverage increased sharply around key geopolitical events, including the Euromaidan protests (2013-2014), the annexation of Crimea (2014), and Russia's full-scale invasion of Ukraine (2022). Multilingual clustering, in combination with manual annotation, identifies recurring patterns in coverage, including geopolitical tensions between Russia and the West, energy security, and disinformation campaigns. Cluster linking reveals how these patterns evolved over time in response to major events: clusters in 2013-2014 focused on the Russia-Ukraine gas dispute, the Euromaidan protests, and Crimea, while those in 2021-2022 centred on the full-scale invasion and its global repercussions. Entity analysis shows that media attention consistently concentrated on a small set of political figures, including Vladimir Putin, Volodymyr Zelenskyy, and Donald Trump, whose prominence varied across different phases of the conflict and key political events. Analysis of Russian media further indicates sustained publication in foreign languages, with increases during key geopolitical moments and differences in how the conflict is contextualised across language groups. Despite uneven regional and linguistic coverage, this study provides a large-scale, long-term, and multilingual overview of media coverage. Future research could extend this work through cross-country comparisons, linking narrative shifts to external variables, such as public opinion data or electoral outcomes, and applying the methodology to other large-scale datasets.