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.

In many competitive sports and games (such as tennis, basketball, chess and electronic sports), the most useful definition of a competitor's skill is the propensity of that competitor to win against an opponent. It is often difficult to measure this skill explicitly: take basketball for example, a team's skill depends on the abilities of its players in terms of shooting accuracy, physical fitness, mental preparation, but also on the team's cohesion and coordination, on its strategy, on the enthusiasm of its fans, and a number of other intangible factors. However, it is easy to observe this skill implicitly through the outcomes of matches. In this setting, probabilistic models of pairwise-comparison outcomes provide an elegant and effective approach to quantifying skill and to predicting future match outcomes given past data. These models, pioneered by Zermelo [1928] in the context of chess (and by Thurstone [1927] in the context of psychophysics), have been studied for almost a century. They posit that each competitor i (i.e., a team or player) is characterized by a latent score s R and that the outcome probabilities of a match between i and j are a function of

The Internet has enabled the emergence of massive online collaborative projects. As the number of contributors to these projects grows, it becomes increasingly important to understand and predict whether the edits that users make will eventually impact the project positively. Existing solutions either rely on a user reputation system or consist of a highly-specialized predictor tailored to a specific peer-production system. In this work, we explore a different point in the solution space, which does not involve any content-based feature of the edits. To this end, we formulate a statistical model of edit outcomes. We view each edit as a game between the editor and the component of the project. We posit that the probability of a positive outcome is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. Then, we consider Wikipedia and the Linux kernel, two examples of large-scale collaborative projects, and we seek to understand whether this simple model can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. Furthermore, inspecting the model parameters enables us to discover interesting structure in the data. Our method is simple to implement, computationally inexpensive, and it produces interpretable results; as such, we believe that it is a valuable tool to analyze collaborative systems.