"A Special Operation": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems’ Coverage of the Russo-Ukrainian War
Keywords:Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Analysis of the relationship between social media and mainstream media, Qualitative and quantitative studies of social media, Social network analysis; communities identification; expertise and authority discovery
AbstractThe coverage of the Russian invasion of Ukraine has varied widely between Western, Russian, and Chinese media ecosystems with propaganda, disinformation, and narrative spins present in all three. By utilizing the normalized pointwise mutual information metric, differential sentiment analysis, word2vec models, and partially labeled Dirichlet allocation, we present a quantitative analysis of the differences in coverage amongst these three news ecosystems. We find that while the Western press outlets have focused on the military and humanitarian aspects of the war, Russian media have focused on the purported justifications for the “special military operation” such as the presence in Ukraine of “bio-weapons” and “neo-nazis”, and Chinese news media have concentrated on the conflict’s diplomatic and economic consequences. Detecting the presence of several Russian disinformation narratives in the articles of several Chinese media outlets, we finally measure the degree to which Russian media has influenced Chinese coverage across Chinese outlets’ news articles, Weibo accounts, and Twitter accounts. Our analysis indicates that since the Russian invasion of Ukraine, Chinese state media outlets have increasingly cited Russian outlets as news sources and spread Russian disinformation narratives.
How to Cite
Hanley, H. W. A., Kumar, D., & Durumeric, Z. (2023). "A Special Operation": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems’ Coverage of the Russo-Ukrainian War. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 339-350. https://doi.org/10.1609/icwsm.v17i1.22150