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The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of \u201cfascism\/Nazism\u201d in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-024-00479-w","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T13:03:13Z","timestamp":1718197393000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Analyzing Russia\u2019s propaganda tactics on Twitter using mixed methods network analysis and natural language processing: a case study of the 2022 invasion of Ukraine"],"prefix":"10.1140","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6270-8985","authenticated-orcid":false,"given":"Iuliia","family":"Alieva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0829-3341","authenticated-orcid":false,"given":"Ian","family":"Kloo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6356-0238","authenticated-orcid":false,"given":"Kathleen M.","family":"Carley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"479_CR1","unstructured":"Woolley S, Howard P (2017) Computational propaganda worldwide: executive summary. 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