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US federal legislation is a common subject of discussion and advocacy on the web, inspired by the open government movement. While the contents of these bills are freely available for download, understanding them is a significant challenge to experts and average citizens alike due to their length, complex language, and obscure topics. To make these important documents more accessible to the general public, we present Many Bills (http://manybills.us): a web-based set of visualization tools that reveals the underlying semantics of a bill. Using machine learning techniques, we classify each bill’s sections based on existing document-level categories. We then visualize the resulting topic substructure of these bills. These visualizations provide an overview-and-detail view of bills, enabling users to read individual sections of a bill and compare topic patterns across multiple bills. Through an overview of the site’s user activity and interviews with active users, this paper highlights how Many Bills makes the tasks of reading bills, identifying outlier sections in bills, and understanding congressperson’s legislative activity more manageable.
By: Yannick Assogba; Irene Ros; Joan M. DiMicco; Matt McKeon
Published in: CHI '11: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems NY, NY, , ACM, p.433-42 (doi>10.1145/1978942.1979004) in 2011
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