Tomila Lankina and Katerina Tertytchnaya will be presenting their work on the effects of regional protests on public opinion at the 112th Annual Meeting of the American Political Science Association which will take place from September 1-4th, 2016 in Philadelphia.The paper analyzes the extent to which the 2011-2012 sub-national electoral protests in Russia swayed public opinion towards the protesters’ demands. An earlier version of this paper had been also presented at the Annual Meeting of the European Political Science Association (EPSA) in Brussels in June 2016.
A new research paper by Rodion Skovoroda and Tomila Lankina entitled Fabricating Votes for Putin: New Tests of Fraud and Electoral Manipulations from Russia has been published in Post-Soviet Affairs.
Katerina Tertytchnaya, who analyses protest trends in Russia for the project, has recently been awarded a Fulbright-Schuman Fellowship to conduct research at Columbia University in New York. During this time, Katerina will research how economic sanctions affected domestic politics and presidential approval in Russia. She will continue to work closely with the Popular Mobilization project team, bringing together evidence from public opinion, media analysis and protest trends. The Fulbright-Schuman Program is jointly financed by the U.S. State Department and the Directorate-General for Education and Culture of the European Commission.
Tomila Lankina has been recently an invited speaker at the Transatlantic Academy in Washington, DC where she gave a talk on popular mobilization in Russia and on Russia’s media manipulation in the Russia-Ukraine conflict.
Russian President Vladimir Putin’s confrontational foreign policy continues to impose high costs on his country, contributing to economic decline, continued corruption, and political isolation. A shrinking circle of people around the Kremlin is involved in opaque decision-making while economic and social problems are given less consideration than political and security issues. To what extent is Russia’s regressive path sustainable? What is the role of Russian elites both at home and in exile in influencing the policies of the Russian government in the short and medium-term? How do European governments and business communities assess the sustainability of the Putin system? Should we expect a rise in socio-economic and political discontent in the coming months leading up to Russia’s fall 2016 parliamentary elections?
To discuss these issues, the LSE International Relations Department will be holding a roundtable panel involving leading experts on Russian domestic and foreign policies including fellows from the Transatlantic Academy in Washington, DC.
Coverage of the Euromaidan protests by select Russian state-controlled media (Rossiyskaya gazeta, Komsomolskaya pravda, Izvestiya newspapers; and Russia 1, Channel 1 and NTV TV channels); and independent Russian and Ukrainian media sources (Rosbalt, Interfax and Zerkalo nedeli). Lower values (“score” line) represent a tendency to portray protests as disorder, while higher values indicate a freedom to protest trend in the media framing of protests. K1-K4 represent spikes in media coverage of protest.
Data for the Russian Protests Dataset is collected from the namarsh.ru website. The data cover protests ranging from small-scale acts to large-scale demonstrations featuring tens of thousands of protesters. For each protest event we collect information regarding the following: (i) the date and location of the protest event; (ii) type of protest, such as whether the protest event is a strike, demonstration, march, occupation or other; (iii) number of participants, where such information is available; and (iv) whether police repression and/or other forms of disruption were used during the event. The dataset does not include events organized by the ruling United Russia party or by pro-government youth movements like Nashi.
Continue reading “Description of Protest Data”
For construction of the Russian protest framing dictionary we employed a technique called supervised Latent Semantic Scaling (LSS). This supervised machine-learning technique requires manual coding involving a training set for dictionary construction and a test set for dictionary validation. In the manual content analysis stage of the Russian-language protest framing dictionary construction, each sentence of the randomly-selected thirty news stories was coded by the lead project researcher for the analysis of the framing of protests in Russian state-controlled media on a five-point scale by the primary coder, and then sentence scores were aggregated into document scores by taking the average. Sentence-level coding is usually necessary in document scaling considering that human coders cannot make nuanced judgements reliably (c.f. Benoit et al. 2015).
Continue reading “Dictionary Construction Procedure”
The Russian protest framing dictionary was created to analyse how Russian state-controlled media cover street protests. The list of keywords in the dictionary and continuous scores attached to the words allow computer programs to locate Russian language news stories on a social disorder vs. freedom to protest scale.
The dictionary was constructed using a technique called Latent Semantic Scaling. It is based on a 27 million-word corpus of Russian newspaper articles and TV transcripts published in state-controlled media sources in 2011-2014 (NTV, Russia 1, Channel 1, Izvestia, Russian Gazette and Komsomolskaya Pravda). The dictionary is able to capture the framing of protest on a par with human coders. Nevertheless, caution needs to be exercised when applying this dictionary to analysis of news stories collected from different time periods or with different types of media content.
Use of the dictionary is very simple and is similar to other forms of dictionary-based content analysis. Document scores should be calculated ignoring words not found in the dictionary. In other words, the document scores are a sum of scores divided by the number of entry words in the documents, not by the total number of words in the documents. Please see the sample code for more detail.
This is a sample R code for dictionary-based content analysis. You have to install qunteda package before running this code.
Continue reading “Content Analysis Employing the LSS Dictionary”