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Scientific article
Open access
English

Protest event analysis: developing a semiautomated nlp approach

Publication date2021
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.

Keywords
  • Protest event analysis
  • Natural language processing
  • Newswires
  • Europe
  • Great Recession
Citation (ISO format)
LORENZINI, Jasmine et al. Protest event analysis: developing a semiautomated nlp approach. In: American Behavioral Scientist, 2021.
Main files (1)
Article (Published version)
accessLevelPublic
Identifiers
  • PID : unige:152063
ISSN of the journal0002-7642
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362downloads

Technical informations

Creation06/02/2021 11:10:00 AM
First validation06/02/2021 11:10:00 AM
Update time03/16/2023 12:41:37 AM
Status update03/16/2023 12:41:36 AM
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