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Social media platforms provide continuous access to user generated contentthat enables real-time monitoring of user behavior and of events. Thegeographical dimension of such user behavior and events has recently caught alot of attention in several domains: mobility, humanitarian, orinfrastructural. While resolving the location of a user can be straightforward,depending on the affordances of their device and/or of the application they areusing, in most cases, locating a user demands a larger effort, such asexploiting textual features. On Twitter for instance, only 2% of all tweets aregeo-referenced. In this paper, we present a system for zoomed-in grounding(below city level) for short messages (e.g., tweets). The system combinesdifferent natural language processing and machine learning techniques toincrease the number of geo-grounded tweets, which is essential to manyapplications such as disaster response and real-time traffic monitoring.

QT2S: A System for Monitoring Road Traffic via Fine Grounding of Tweets