Analyzing large quantities of textual data can be a time consuming and daunting task. For this project, we pulled in a subset of the 2006 VAST Challenge dataset, which consisted of almost 250 fictitious news stories from the fictitious town of Alderwood. As analysts, our goal was to determine if anything fishy was going on in this town. To help enable this, we built a tool using D3, leveraging a force layout with entities as nodes to enable high-level navigation of the document space. The force layout leverages the Gestalt principle of proximity to suggest groups of entities that are likely to have a causal connection or other significant relationship. In this way, the tool also supported hypothesis generation by suggesting clusters of entities that merited further investigation. Additionally, access to raw document text and the timeline, then enables further interrogation and either the acceptance or rejection of information within one’s hypotheses. The visual nature of the force layout also supports random discovery of nodes that might seem interesting to humans, but that a machine would not identify as being of interest. For example, what the heck is the FDA investigating in Alderwood?
Date: Spring 2018
Course: CS8801 Visual Design Analysis
Skills: JavaScript, HTML, CSS