Name
Abstract | ||
|---|---|---|
ABSTRACT The European Union law-making process is an instance of a peer-production system. We mine a rich dataset of law edits and introduce models predicting their adoption by parliamentary committees. Edits are proposed by parliamentarians, and they can be in conflict with edits of other parliamentarians and with the original proposition in the law. Our models combine three different categories of features: (a) Explicit features extracted from data related to the edits, the parliamentarians, and the laws, (b) latent features that capture bi-linear interactions between parliamentarians and laws, and (c) text features of the edits. We show experimentally that this combination enables us to accurately predict the success of the edits. Furthermore, it leads to model parameters that are interpretable, hence provides valuable insight into the law-making process. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3442381.3450131 | International World Wide Web Conference |
Keywords | DocType | Citations |
discrete choice models, natural language processing, computational social science, data-driven political science, web mining | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Kristof | 1 | 1 | 2.39 |
| Aswin Suresh | 2 | 0 | 0.34 |
| Matthias Grossglauser | 3 | 0 | 0.68 |
| Patrick Thiran | 4 | 2712 | 217.24 |