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Computational approaches to mapping interest group representation: a test and discussion of different methods

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Abstract

Studying patterns of interest representation in politics is a central concern of scholars working on interest groups and lobbying. However, systematic empirical analysis of interest group representation entails a large amount of coding and is potentially prone to error. This letter addresses the potential of two computational methods in enabling large-scale analyses of interest group representation. We discuss the trade-offs associated with each method and empirically compare a manual, a query-based, and an off-the-shelf supervised machine learning approach to identify interest groups in a sample of 3000 news stories. Our results demonstrate the potential of automated methods, especially when used in combination.

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Notes

  1. See for documentation and source: https://cloud.google.com/natural-language/docs/analyzing-entities.

  2. The coding of group types is based on the INTERARENA coding scheme (see Binderkrantz et al. 2020).

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Authors and Affiliations

  1. University of Amsterdam, Amsterdam, Netherlands

    Ellis Aizenberg

  2. Aarhus University, Aarhus, Denmark

    Anne Skorkjӕr Binderkrantz

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  1. Ellis Aizenberg
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  2. Anne Skorkjӕr Binderkrantz
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Correspondence to Ellis Aizenberg.

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The original online version of this article was revised due to a retrospective Open Access cancellation.

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Aizenberg, E., Binderkrantz, A.S. Computational approaches to mapping interest group representation: a test and discussion of different methods. Int Groups Adv 10, 181–192 (2021). https://doi.org/10.1057/s41309-021-00121-4

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