Abstract
The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.
Original language | English |
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Title of host publication | Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 |
Editors | Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti |
Publisher | CSREA Press |
Pages | 137-143 |
Number of pages | 7 |
ISBN (Electronic) | 1601324383, 9781601324382 |
Publication status | Published - 2016 |
Event | 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 - Las Vegas, United States Duration: 25 Jul 2016 → 28 Jul 2016 |
Publication series
Name | Proceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 |
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Conference
Conference | 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 |
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Country/Territory | United States |
City | Las Vegas |
Period | 25/07/16 → 28/07/16 |
Bibliographical note
Publisher Copyright:CSREA Press ©.
Keywords
- Disaster management
- Machine learning
- Risk assessment
- Social media analysis
- Text classification
ASJC Scopus subject areas
- Software
- Artificial Intelligence