Text Network Analysis of Russian Presidents' Speeches 2008-2012

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Addresses to the Federal Assembly of the Russian Federation by Russian Presidents. 2008 to 2012. Comparative Analysis. by Dmitry Paranyushkin, Nodus Labs dmitry@noduslabs.com | www.noduslabs.com | Berlin, Germany

Introduction Every year Russian presidents deliver address to the Federal Assembly of the Russian Federation, which includes prominent politicians, ministers, government officials and various other public figures. They set the president’s public agenda for the forthcoming year and give an insight into the specific proposals and strategies that are to be implemented. These addresses are available in English on Kremlin’s website http:// eng.kremlin.ru/transcripts/messages

Objective For this research project we at Nodus Labs decided to apply our text network analysis methodology to the four addresses by President Dmitry Medvedev (given from 2008 through 2011) and the first address by President Vladimir Putin in his third term (given in 2012). Our interest was to identify the most prominent topics in these addresses and also detect any shifts in rhetorics during Medvedev’s presidency and especially at the moment of transition from Medvedev to Putin.

Methodology In this work we used the methodology for text network analysis developed by Dmitry Paranyushkin from Nodus Labs. Textual analysis is performed by Textexture software (http://textexture.com) developed at Nodus Labs. The visualizations and some parts of graph analysis are produced by the open-source Gephi software (http://gephi.org) as well as Sigma.Js library by Alexis Jacomy. A detailed description of methodology we use for text network analysis and visualization is available in the white paper published on http://noduslabs.com/research/pathwaysmeaning-circulation-text-network-analysis/ To give a short introduction, here’s a brief description of how it works. First, a submitted text is scanned to remove all the most frequently used “stopwords”, such as “are”, “is”, “the”, “a”, etc. The second scan removes any extra characters and turns every word into its morpheme (e.g. “took” becomes “take”, “plates” becomes “plate”). The resulting sequence is then scanned so that every word is encoded as a node and their co-occurrence is encoded as the connection between them. The nodes are not only linked if they are next to each other in the text. The paragraph and sentence structures are taken into account, as


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