Story Tracker : incremental visual text analytics of news story development

Abstract

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system’s capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.