Abstract
Protest event analysis is a key method to study social movements, allowing to systematically analyze protest events over time and space. However, the manual coding of protest events is time-consuming and resource intensive. Recently, advances in automated approaches offer opportunities to code multiple sources and create large data sets that span many countries and years. However, too often the procedures used are not discussed in details and, therefore, researchers have a limited capacity to assess the validity and reliability of the data. In addition, many researchers highlighted biases associated with the study of protest events that are reported in the news. In this study, we ask how social scientists can build on electronic news databases and computational tools to create reliable PEA data that cover a large number of countries over a long period of time. We provide a detailed description our semiautomated approach and we offer an extensive discussion of potential biases associated with the study of protest events identified in international news sources.
Highlights
The analysis of contentious politics has a long tradition in social science
We ask how social scientists can build on electronic news databases and computational tools to create reliable protest event analysis (PEA) data that cover a large number of countries over a long period of time
We have described our procedure for identifying and coding protest events in 30 European countries during the period 2000-2015
Summary
The analysis of contentious politics has a long tradition in social science. One of the key methods in this field is protest event analysis (PEA), a form of content analysis that allows to systematically collect, quantify, and process large amounts of information on American Behavioral Scientist 00(0)political protest across time and countries (Hutter, 2014a, 2014b). Protest event data has usually relied on newspaper coverage as data source, and, mostly on manual coding. The manual coding of protest events across time and space is intensely time consuming. This poses an important challenge to study large numbers of countries and to cover extensive periods of time. We ask how social scientists can build on electronic news databases and computational tools to create reliable PEA data that cover a large number of countries over a long period of time. Answering this research question requires addressing two challenges: (a) building a procedure to identify unique protest events across multiple news sources and (b) assessing the reliability and validity of the collected data
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