VisCAT: spatio-temporal visualization and aggregation of categorical attributes in twitter data

Abstract

In the last few years, Twitter data has become so popular that it is used in a rich set of new applications, e.g., real-time event detection, demographic analysis, and news extraction. As user-generated data, the plethora of Twitter data motivates several analysis tasks that make use of activeness of 271+ Million Twitter users. This demonstration presents VisCAT; a tool for aggregating and visualizing categorical attributes in Twitter data. VisCAT outputs visual reports that provide spatial analysis through interactive map-based visualization for categorical attributes---such as tweet language or source operating system---at different zoom levels. The visual reports are built based on user-selected data in arbitrary spatial and temporal ranges. For this data, VisCAT employs a hierarchical spatial data structure to materialize the count of each category at multiple spatial levels. We demonstrate VisCAT, using real Twitter dataset. The demonstration includes use cases on tweet language and tweet source attributes in the region of Gulf Arab states, which can be used for deducing thoughtful conclusions on demographics and living levels in local societies.