Studying Lobby Influence in the European Parliament
Aswin Suresh Lazar Radojevic Francesco Salvi Antoine Magron Victor Kristof Matthias Grossglauser
We present a method based on natural language processing (NLP), for studying
the influence of interest groups (lobbies) in the law-making process in the
European Parliament (EP). We collect and analyze novel datasets of lobbies'
position papers and speeches made by members of the EP (MEPs). By comparing
these texts on the basis of semantic similarity and entailment, we are able to
discover interpretable links between MEPs and lobbies. In the absence of a
ground-truth dataset of such links, we perform an indirect validation by
comparing the discovered links with a dataset, which we curate, of retweet
links between MEPs and lobbies, and with the publicly disclosed meetings of
MEPs. Our best method achieves an AUC score of 0.77 and performs significantly
better than several baselines. Moreover, an aggregate analysis of the
discovered links, between groups of related lobbies and political groups of
MEPs, correspond to the expectations from the ideology of the groups (e.g.,
center-left groups are associated with social causes). We believe that this
work, which encompasses the methodology, datasets, and results, is a step
towards enhancing the transparency of the intricate decision-making processes
within democratic institutions.