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

Abstract

Social media platforms provide continuous access to user generated content that enables real-time monitoring of user behavior and of events. The geographical dimension of such user behavior and events has recently caught a lot of attention in several domains: mobility, humanitarian, or infrastructural. While resolving the location of a user can be straightforward, depending on the affordances of their device and/or of the application they are using, in most cases, locating a user demands a larger effort, such as exploiting textual features. On Twitter for instance, only 2% of all tweets are geo-referenced. In this paper, we present a system for zoomed-in grounding (below city level) for short messages (for example, tweets). The system combines different natural language processing and machine learning techniques to increase the number of geo-grounded tweets, which is essential to many applications such as disaster response and real-time traffic monitoring.

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