Abstract
We address the question of how can publicly accessible information be used to make a map of the political actors and their leanings, that would benefit both policy makers and stakeholders in the European Commission’s ‘Better regulation agenda’ and contribute to social stability. We explore this possibility by using data from the Transparency Register and the open public consultations of the European Commission in the area of Banking and Finance. We compare lobbying organizations active in this area according to three criteria: (i) their formal categorization in the Transparency Register, (ii) their self-declared goals and activities, and (iii) their leanings towards policy issues as derived from their responses to public consultations. We combine methods from information retrieval, text mining, and network analysis to obtain insights on the policy arena. We find that constructing a similarity network based on preference patterns adds a crucial dimension in the understanding of how lobby organizations engage in the policy making process.
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Using a language detector from the LATINO text mining library (https://github.com/LatinoLib/LATINO).
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The network is constructed and analyzed in Gephi (https://gephi.org/) [3].
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The \(F_1\) score is a special case of Van Rijsbergen’s effectiveness measure [13], where precision and recall can be combined with different weights.
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Sankey diagrams (https://developers.google.com/chart/interactive/docs/gallery/sankey) are based on work by Google (https://developers.google.com/terms/site-policies).
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Acknowledgements
The authors acknowledge the financial support from the European Union’s Horizon 2020 FET projects DOLFINS (grant no. 640772) and OpenMaker (grant no. 687941), and from the Slovenian Research Agency (research core funding no. P2-0103).
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Sluban, B., Smailović, J., Novak, P.K., Mozetič, I., Battiston, S. (2018). Mapping Organizations’ Goals and Leanings in the Lobbyist Network in Banking and Finance. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_93
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