Irene Ros

Designing and building software, tools and experiences on the web that aim to improve people's lives.

US Federal legislation is a hot topic for discussion and advocacy on the web. Yet legislative bills present a significant challenge for both experts and average citizens to navigate and understand. To explore solutions to this problem, we have created DocBlocks: a prototype visualization and website that enables users to explore the content of congressional bills and communicate their findings to others. Our technique enables us to take any document from a categorized corpus, classify its sections, and visualize its topic structure. With the launch of this service, we hope to provide a valuable tool for open governance and learn from our users at this critical intersection of visualization, advocacy, social software, and civil society. %div.pub

US Federal Legislation is a common subject of discussion and advocacy on the web.The contents of bills present a significant challenge to both experts and average citizens due to their length and complex legal language. To make bills more accessible to the general public, we present Many Bills: a web-based visualization prototype that reveals the underlying semantics of a bill. We classify the sections of a bill into topics and visualize them using different colors. Further, using information retrieval techniques, we locate sections that don't seem to fit with the overall topic of the bill. To highlight outliers in our 'misfit mode', we visualize them in red, which builds a contrast against the remaining gray sections. Both topic and misfit 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. We obtained initial user feedback and continue collecting label corrections from users through the interface.

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.