The News Auditor: Visual Exploration of Clusters of Stories

Abstract

In recent years, the quantity of content generated by news agencies and blogs is constantly growing, making it difficult for readers to process and understand this overwhelming amount of data. Online news aggregators present clusters of similar stories in a simple, list-based manner, where the most important article is shown first, while all the other similar articles appear below as hyperlinked headlines. This layout makes the user unaware of the content differences between articles, thus making it very difficult to get a comprehensive picture. Understanding what was changed, how, when and by whom, would lead to new insights about the content distribution over the internet and help in dealing with the news overload problem. We present a visual analytics tool that allows the user to compare the articles that belong to the same story and understand the differences at three levels of detail. Story matrix provides an overview of a document cluster, where the user can identify articles of interest based on their overall similarity and reorder them by different criteria. Structural view shows document thumbnails with highlighted paragraphs of the text that were copied, modified or repositioned by different sources. Finally, Document level view presents two articles side by side to provide full-text comparison. To evaluate our tool, we present two user scenarios applied on a real world data set.