Studying Lobby Influence in the European Parliament

TL;DR

This paper introduces an NLP-based method to analyze and interpret the influence of interest groups on European Parliament members by comparing their texts and validating links through indirect measures.

Contribution

It presents a novel NLP approach, datasets, and validation techniques to uncover and analyze lobby influence in the European Parliament, enhancing transparency.

Findings

Best method achieves an AUC score of 0.77

Discovered links align with political group ideologies

Method outperforms several baselines

Abstract

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.