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

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review


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.

TidsskriftInterest Groups and Advocacy
Sider (fra-til)181-192
Antal sider12
StatusUdgivet - jun. 2021

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