TY - GEN
T1 - Identifying informative messages in disaster events using Convolutional Neural Networks
AU - Caragea, Cornelia
AU - Silvescu, Adrian
AU - Tapia, Andrea H.
N1 - Funding Information:
We thank the National Science Foundation for support from the grants IIS #1526542 and IIS #1526678 to Cornelia Caragea and Andrea Tapia. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the National Science Foundation. We also wish to thank our anonymous reviewers for their constructive comments.
PY - 2016
Y1 - 2016
N2 - Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the "bag of words" and n-grams as features on several datasets of messages from flooding events.
AB - Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the "bag of words" and n-grams as features on several datasets of messages from flooding events.
UR - http://www.scopus.com/inward/record.url?scp=85015806207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015806207&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85015806207
T3 - Proceedings of the International ISCRAM Conference
BT - ISCRAM 2016 Conference Proceedings - 13th International Conference on Information Systems for Crisis Response and Management
A2 - Antunes, Pedro
A2 - Banuls Silvera, Victor Amadeo
A2 - Porto de Albuquerque, Joao
A2 - Moore, Kathleen Ann
A2 - Tapia, Andrea H.
PB - Information Systems for Crisis Response and Management, ISCRAM
T2 - 13th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2016
Y2 - 22 May 2016 through 25 May 2016
ER -