Visualizing User-Defined, Discriminative Geo-Temporal Twitter Activity


  • Ingmar Weber Qatar Computing Research Institute
  • Venkata Rama Kiran Garimella Qatar Computing Research Institute



We present a system that visualizes geo-temporal Twitter activity. The distinguishing features our system offers include, (i) a large degree of user freedom in specifying the subset of data to visualize and (ii) a focus on *discriminative* patterns rather than high volume patterns. Tweets with precise GPS co-ordinates are assigned to geographical cells and grouped by (i) tweet language, (ii) tweet topic, (iii) day of week, and (iv) time of day. The spatial resolutions of the cells is determined in a data-driven manner using quad-trees and recursive splitting. The user can then choose to see data for, say, English tweets on weekend evenings for the topic "party". This system has been implemented for 1.8 million geo-tagged tweets from Qatar ( and for 4.8 million geo-tagged tweets from New York City ( and can be easily extended to other cities/countries.




How to Cite

Weber, I., & Garimella, V. R. K. (2014). Visualizing User-Defined, Discriminative Geo-Temporal Twitter Activity. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 656-657.