Caroline Berger, Hanjun Xian, Krishna Madhavan, Niklas Elmqvist

We propose Textiverse, a big data approach for mining geotagged timestamped textual data on a map, such as for Twitter feeds, crime reports, or restaurant reviews. We use a scalable data management pipeline that extracts keyphrases from online databases in parallel. We speed up this time-consuming step so that it outpaces the content creation rate of popular social media. The result is presented in a web-based interface that integrates with Google Maps to visualize textual content of massive scale. The visual design is based on aggregating spatial regions into discrete sites and rendering each such site as a circular tag cloud. To demonstrate the intended use of our technique, we first show how it can be used to characterize the U.S.\ National Science Foundation funding status based on all 489,151 awards. We then apply the same technique on visually representing a more spatially scattered and linguistically informal dataset: 1.2 million Twitter posts about the Android mobile operating system.