Learning VAA: A new method for matching users to parties in voting advice applications
In: Journal of elections, public opinion and parties, Band 32, Heft 2, S. 339-357
ISSN: 1745-7297
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In: Journal of elections, public opinion and parties, Band 32, Heft 2, S. 339-357
ISSN: 1745-7297
This paper argues that current voting advice applications (VAAs) do not sufficiently fulfil their stated aim of increasing voters' political competence. First, we define four criteria to evaluate whether their methods are likely to increase political competence: informativeness, respect for users' way of comparing and aggregating policy issues, reliability, and transparency. Second, we argue that current VAAs compare and aggregate users' and parties' policy preferences following a weak method that fails in two of them. To prove it, we analyse the methodology of most currents VAAs and use the outcomes from the EU-Vox 2014 in several countries. Third, we discuss two possibilities by which VAAs could improve: (1) by using ex-ante survey data to fill their gaps, or (2) by creating a learning algorithm to adapt the VAA to users' preferences. We found that some changes need to be made if VAAs aim to have an impact on users' political competence.
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