Our framing analysis tool is now publicly available in the LSS package. We performed analysis of Russian media’s framing of street protests using a system developed in Python, but subsequently transferred the ‘trained’ model into R to make it more accessible. We labelled it ‘dictionary’ earlier, but refer to it now as a fitted Latent Semantic Scaling model. Applying the model to news stories one can easily produce plots that are very similar to those in our papers. The values of the score are high when a news article contains framing of street protests as “freedom to protest” and when the score is low, protests are framed as “social disorder.”
Katerina Tertytchnaya and Tomila Lankina, 20 September 2016
In recent years, economic hardship in Russia has led to an increase in industrial and socioeconomic protest activity across regions. Protests over wage arrears, strikes and hunger strikes were particularly prominent in the first 8 months of the current year. And although the Russian Communist Party (KPRF) is particularly active in coordinating protest events across Russia’s regions, many protest events are spontaneous and grass-roots based. Overall, beyond labor protests, Russians appear very concerned about housing issues and the increase in prices for services such as transport. In this post we analyze variation in protest activity over time and across types of protests to study whom protesters pursuing various causes blame. We do so by studying where protest events take place (for example, whether they take place in front of the local town hall or regional parliament), the slogans and expressions protesters use, the images and text on their placards and posters, as well as journalistic descriptions of the events. Protest data for this analysis are harvested from namarsh.ru. Focusing on blame attribution during protest events in order to understand public opinion has several advantages over employing more abstract concepts like economic voting patterns. (On the question and analysis of Russian protests in recent years, see a report by CEPR). The protest categories we employ here further allow us to study patterns of blame attribution depending on the causes advanced in each of the events.
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”