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.

Reading congressional legislation, also known as bills, is often tedious because bills tend to be long and written in complex language. In IBM Many Bills, an interactive web-based visualization of legislation, users of different backgrounds can browse bills and quickly explore parts that are of interest to them. One task users have is to be able to locate sections that don't seem to fit with the overall topic of the bill. In this paper, we present novel techniques to determine which sections within a bill are likely to be outliers by employing approaches from information retrieval. The most promising techniques first detect the most topically relevant parts of a bill by ranking its sections, followed by a comparison between these topically relevant parts and the remaining sections in the bill. To compare sections we use various dissimilarity metrics based on Kullback-Leibler Divergence. The results indicate that these techniques are more successful than a classification based approach. Finally, we analyze how the dissimilarity metrics succeed in discriminating between sections that are strong outliers versus those that are 'milder' outliers.