Benjamin Shultz
ABSTRACT
Russia's war in Ukraine has marked an inflection point for the future of the global order and democracy itself. Widely condemned for waging a war of aggression, the Russian government has used its official social media channels to spread disinformation as justification for the war. This study examines how the Russian government has used its official Twitter accounts to shape English-language conversations about the war in Ukraine. 2,685 English-language tweets posted by 70 Russian government accounts between 1 September 2022 and 31 January 2023 were analyzed using BERTopic. Initial topic analysis shows the Russian government portrayed itself as a noble world leader interested in peace and cooperation, while deflecting blame onto the “Kiev Regime” for starting the war. A semantic similarity analysis was then conducted to compare the narratives originating from Russian government Twitter accounts to 149,732 English-language tweets about the war in Ukraine to estimate these narratives’ spread. Results show a segment of general discussion tweets to exhibit strongly similar language to Russian government tweets, but also highlight differences between the frequency and saliency of Russian government narratives. This work contributes one of the first analyses of disinformation originating from official Russian government social media channels about the war in Ukraine.
- OECD. 2022. OECD Policy Responses on the Impacts of the War in Ukraine. OECD Publishing, Paris, France. DOI:https://doi.org/10.1787/37186bde-enGoogle Scholar
- Craig Silverman & Jeff Kao. 2022. Infamous Russian Troll Farm Appears to Be Source of Anti-Ukraine Propaganda. (March 2022). Retrieved January 21, 2023 from: https://www.propublica.org/article/infamous-russian-troll-farm-appearsto-be-source-of-anti-ukraine-propagandaGoogle Scholar
- Joanna van der Merwe. 2022. Twitter in a Time of War: Russia Sanctions Should Include Social Media. (April 2022). Retrieved January 21, 2023 from: https://cepa.org/article/twitter-in-a-time-of-war-russia-sanctions-shouldinclude-social-media/Google Scholar
- Twitter. 2022. Our ongoing approach to the war in Ukraine. (March 2022). Retrieved January 21, 2023 from: https://blog.twitter.com/en_us/topics/company/2022/our-ongoing-approach-tothe-war-in-ukraineGoogle Scholar
- Zeve Sanderson, Megan Brown, Richard Bonneau, Jonathan Nagler, and Joshua Tucker 2021. Twitter flagged Donald Trump's tweets with election misinformation: They continued to spread both on and off the platform. Harvard Kennedy School Misinformation Review 2, 4 (August 2021), 19 pages. DOI:https://doi.org/10.37016/mr-2020-77Google Scholar
- Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (March 2018), 5 pages. DOI:https://doi.org/10.1126/science.aap9559Google ScholarCross Ref
- Timothy Graham and Jay D. Thompson. 2022. Russian government accounts are using a Twitter loophole to spread disinformation. Retrieved January 21, 2023 from: https://theconversation.com/russian-government-accounts-are-using-atwitter-loophole-to-spread-disinformation-178001Google Scholar
- Scott Came. 2018. Topic Modeling and Visualization of Russian IRA Tweets. (August 2018). Retrieved January 23, 2022 from: https://www.cascadiaanalytics.com/2018/08/12/ira-tweets1.htmlGoogle Scholar
- Dan T. Miller. 2018. Topics and emotions in Russian Twitter propaganda. (August 2018). Retrieved January 23, 2023 from: https://firstmonday.org/ojs/index.php/fm/article/download/9638/7785Google Scholar
- Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a classbased TF-IDF procedure. arXiv:2203.05794. Retrieved from: https://arxiv.org/abs/2203.05794.Google Scholar
- Aleksandra Urman and Mykola Makhortykh. 2022. My war is your special operation: Engagement with pro- and anti-regime framing of the war in Ukraine on Russian social media. (September 2022). OSF Preprints, 26 pages. DOI:10.31219/osf.io/67snk.Google Scholar
- Piyush Ghasiya and Kazutoshi Sasahara. 2022. Messaging Strategies of Ukraine and Russia on Telegram during the 2022 Russian Invasion of Ukraine. (November 2022). Research Square, 25 pages. DOI:https://doi.org/10.21203/rs.3.rs-2288409/v2.Google Scholar
- Julio Díaz López and Pranava Madhyastha. 2021. A Focused Analysis of Twitter-based Disinformation from Foreign Influence Operations. In Proceedings of the 1st Workshop on Knowledge Graphs for Online Discourse (KnOD ‘21), April 14, 2021, Virtual Event.Google Scholar
- Zara Ahmad-Post, Sagar Samatani, Hongyi Zhu, and Sue Brown. 2021. Political News Propagation Headline Matching. In Proceedings of the 1st ACM KDD Workshop on Artificial Intelligence-enabled Cybersecurity, August 14-18, 2021, Singapore. ACM, Inc. DOI:https://doi.org/10.1145/1234567890Google ScholarDigital Library
- Dror Walter, Yotam Ophir, and Kathleen Jamieson. 2020. Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015–2017. American Journal of Public Health 110, 5 (May 2020), 6 pages. DOI:10.2105/AJPH.2019.305564Google ScholarCross Ref
- Hans W. A. Hanley, Deepak Kumar, and Zakir Durumeric. 2022. Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit. arXiv:2205.14484. Retrieved from: https://arxiv.org/abs/2205.14484.Google Scholar
- Oxford Internet Institute. 2022. War in Ukraine and Disinformation Newsletter 12 April 2022. (April 2022). Retrieved January 27, 2023 from: https://demtech.oii.ox.ac.uk/war-in-ukraine-and-disinformation-newsletter-12- april-2022/.Google Scholar
- Cagri Toraman, Oguzhan Ozcelik, Furkan Şahinuç, and Fazli Can. 2022. Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees. arXiv: 2210.05401. Retrieved from: https://arxiv.org/abs/2210.05401.Google Scholar
- Kaggle. 2022. Ukraine Conflict Twitter Dataset. Retrieved January 21, 2023 from: https://www.kaggle.com/datasets/bwandowando/ukraine-russian-crisistwitter-dataset-1-2-m-rows.Google Scholar
- Abhijit Balaji. 2019. spacy-langdetect 0.1.2. Retrieved from: https://pypi.org/project/spacy-langdetect/.Google Scholar
- Roman Egger and Joanne Yu. 2022. A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology 7, 886498 (May 2022). DOI:10.3389/fsoc.2022.886498.Google ScholarCross Ref
- Fiona Martin and Mark Johnson. 2015. More Efficient Topic Modelling Through a Noun Only Approach. In Proceedings of the Australasian Language Technology Association Workshop 2015, Parramatta, Australia. ACL Anthology. Retrieved from: https://aclanthology.org/U15-1013/.Google Scholar
- Leland McInnes, John Healy, James Melville 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. Retrieved from: https://arxiv.org/abs/1802.03426.Google Scholar
- Richard E. Bellman. 1961. Adaptive Control Processes. Princeton University Press, Princeton, NJ.Google Scholar
- Inseop Chung, Daesik Kim, and Nojun Kwak. 2021. Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation. arXiv:2102.13002v3. Retrieved from: https://arxiv.org/abs/2102.13002v3.Google Scholar
- Giancarlo Crocetti. 2015. Textual Spatial Cosine Similarity. arXiv:1505.03934. Retrieved from: https://arxiv.org/abs/1505.03934.Google Scholar
- Jörg Forthmann. 2020. Social listening: a potential game changer in reputation management How big data analysis can contribute to understanding stakeholders' views on organisations. Corporate Communications: An International Journal 1356, 3289 (August 2020), 22 pages. DOI:10.1108/CCIJ-01-2020-0028.Google Scholar
- Fabian Pedegrosa, 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (October 2011), 6 pages.Google Scholar
- Shaymaa H. Mohammed and Salam Al-augby. 2020. LSA & LDA Topic Modeling Classification: Comparison study on E-books. Indonesian Journal of Electrical Engineering and Computer Science, 19, 1 (July 2020), 11 pages. DOI:http://doi.org/10.11591/ijeecs.v19.i1.pp.Google ScholarCross Ref
- Dimo Angelov. 2020. Top2Vec: Distributed Representations of Topics. arXiv: 2008.09470. Retrieved from: https://arxiv.org/abs/2008.09470.Google Scholar
Index Terms
- In the Spotlight: The Russian Government's Use of Official Twitter Accounts to Influence Discussions About its War in Ukraine
Recommendations
Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023Online social media represent an oftentimes unique source of information, and having access to reliable and unbiased content is crucial, especially during crises and contentious events. We study the spread of propaganda and misinformation that ...
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceOver the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” ...
Russian trolls speaking Russian: Regional Twitter operations and MH17
WebSci '20: Proceedings of the 12th ACM Conference on Web ScienceThe role of social media in promoting media pluralism was initially viewed as wholly positive as social media could break the oligopoly of (often state-owned) mainstream media. However, some governments are allegedly manipulating social media by hiring ...
Comments