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- W2201451844 abstract "For now more than four decades, quantitative protest event analysis (PEA) has routinely contributed to the testing and refinement of theories on political processes from different perspectives. However, it is commonly agreed that PEA data face serious challenges regarding their data sources. Precisely, researchers applying PEA struggle with the fact that they cannot use multiple sources for large geographical areas and long time periods. As a consequence, most of the scholarship still focuses on a narrow set of European countries or the United States and does not cover the period since the early 2000s. We are bringing PEA and computational linguistics together to suggest and evaluate an approach that will enable political scientists to extend their research designs with a more efficient and at the same time reliable data collection. The approach relies on hidden topic models, word space models, and named entity recognition to identify and code protest events." @default.
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- W2201451844 date "2013-01-01" @default.
- W2201451844 modified "2023-10-16" @default.
- W2201451844 title "Using Computational Linguistics to Enhance Protest Event Analysis" @default.
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- W2201451844 doi "https://doi.org/10.2139/ssrn.2286769" @default.
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