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            Chapter Topic modeling for analysing the Russian propaganda in the conflict with Ukraine

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            Author(s)
            Grassia, Maria Gabriella
            Marino, Marina
            Mazza, Rocco
            Misuraca, Michelangelo
            stavolo, agostino
            Language
            English
            Show full item record
            Abstract
            The conflict between Ukraine and Russia is changing Europe, which is facing a crisis destined to reshape the internal and external relations of the continent, shifting international balances. In this contribution, we show preliminary results on the monitoring of Russian propaganda. In fact, we analysed the content of online newspapers (Strategic Culture Foundation, Global research, News Front, South Front, Katehon, Geopolitics) used as propaganda tools of the Russian government. The newspapers create and amplify the narrative of the conflict, transmitting information filtered by the Kremlin to advance Putin's propaganda about the war. The objective of the work, therefore, is to understand what were the main themes that the Russian media used to motivate the conflict in Ukraine. Specifically, the proposed analysis runs from March 2021, when the Russian military began moving weapons and equipment into Crimea, to the end of March 2022, the day of the first negotiations in Istanbul. In this regard, we used topic modeling techniques to analyse textual content that uncovers the latent thematic structure in document collections to identify emerging topics.
            Book
            ASA 2022 Data-Driven Decision Making
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/161828
            Keywords
            topic modeling; russian propaganda; non-negative matrix factorization; thema EDItEUR::J Society and Social Sciences
            DOI
            10.36253/979-12-215-0106-3.43
            ISBN
            9791221501063
            Publication date and place
            Florence, 2023
            Series
            Proceedings e report,
            Pages
            6
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            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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