Echo

Abstract

The explosive growth of user generated content, along with the continuous increase in the amount of traditional sources of content, has made it extremely hard for users to digest the relevant pieces of information that they need to pay attention to in order to make sense of their needs. Thus, solutions are needed to help both professionals (e.g lawyers, analysts, economists) and ordinary users navigate this flood of information. We present a novel interaction model and system called Echo which uses machine learning techniques to traverse a corpus of documents and distill crucial opinions from the collective intelligence of the crowd. Based on this analysis, Echo creates an intuitive and elegant interface, as though constructed by an editor, that allows users to quickly find salient documents and opinions, all powered by the wisdom of the crowd. The Echo UI directs the user's attention to critical opinions using a natural magazine style metaphor, with visual call outs and other typographic changes. Therefore, this paper present two key contributions (an algorithm and interaction model) that allow a user to "read as normal," while focusing her attention on the important opinions within documents, and showing how these opinions relate to those of the crowd.