Author:
Al Emadi Noora,Abbar Sofiane,Borge-Holthoefer Javier,Guzman Francisco,Sebastiani Fabrizio
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.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献