QT2S: A System for Monitoring Road Traffic Via Fine Grounding of Tweets
| Autor: | Al Emadi, N., Abbar, S., Javier Borge-Holthoefer, Guzman, F., Sebastiani, F. |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Scopus-Elsevier 11th AAAI International Conference on Web and Social Media, pp. 456–459, Montreal, CA, 15-18/May/ 2017 info:cnr-pdr/source/autori:Al Emadi N.; Abbar S.; Borge-Holthoefer J.; Guzman F.V.; Sebastiani F./congresso_nome:11th AAAI International Conference on Web and Social Media/congresso_luogo:Montreal, CA/congresso_data:15-18%2FMay%2F 2017/anno:2017/pagina_da:456/pagina_a:459/intervallo_pagine:456–459 |
| ISSN: | 2334-0770 2162-3449 |
| DOI: | 10.1609/icwsm.v11i1.14925 |
| Popis: | 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 (e.g., 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. Comment: 11th International AAAI Conference on Web and Social Media (ICWSM 2017) |
| Databáze: | OpenAIRE |
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