Understanding Interest Groups’ Information Networks in the European Union using Twitter Data

Who informs who in the complex maze of EU system of interest representation and private actors’ participation in supranational policymaking? Answering this question is key for the legitimacy of the EU system of governance whose input legitimacy depends extensively on getting high-quality and policy-relevant information from a diverse and wide set of stakeholders. Its output legitimacy also depends on the successful implementation of its policies which may be leveraged and improved by an effective communication of supranational decisions to national or local level through the intermediary of representative interest associations. Examining the information transmission networks established between interest groups and policymakers, and amongst interest organizations themselves, taps therefore into the fundamental issue of democratic legitimacy of the EU system of governance. We examine these EU information transmission networks with the help of Twitter data and official information about meetings between interest organizations and EU policymakers. Empirically, we build on the INTEREURO interest groups population dataset to which we add original data gathered by web-scraping interest groups’ Twitter accounts and information about their direct meetings with high-level European Commission officials. This allows identifying directed information ties between analysed private and public actors and examining the patterns and dynamics of communication within different policy networks established across EU policy areas. Theoretically, we develop a theoretical framework that proposes two competing explanations for an interest groups’ decision on whom to follow on Twitter and consequently of an interest groups’ level of centrality in the information transmission networks. The first explanation emphasizes the importance of the insider status of an interest organization and postulates that interest groups are more likely to follow on Twitter organizations with stronger network ties to policymakers, as they are perceived to be closer to decision-making power and therefore assumed to be better informed about policy. The second explanation postulates that that preference similarity is instead a strong predictor of a private actor’s decision about whom to follow/monitor in information networks. Despite its prominence and intuitive appeal, this latter argument has not yet been tested to explore the formation of network ties within the EU interest representation system. We test our theoretical argument with the help of exponential random graph models across then policy sectors, providing the very first comparative, cross-policy domain analysis of EU lobbying information transmission networks. The findings show that preference similarity is a stronger predictor of an organization’s Twitter-following behaviour. Interesting empirical variations are observed across policy sectors regarding (1) the Twitter-following behaviour of organizations and (2) the pattern and frequency of their ties with decision-makers, which we extensively discuss and explain in light of theories of policy networks and information transmission in the context of multilevel governance.