1.1
1
xml
info:srw/schema/1/mods-v3.2
Twitter Mining for Disaster Response: A Domain Adaptation Approach
Hongmin Li
author
Nicolais Guevara
author
Nic Herndon
author
Doina Caragea
author
Kishore Neppalli
author
Cornelia Caragea
author
Anna Squicciarini
author
Andrea H. Tapia
author
2015
University of Agder (UiA)
Kristiansand, Norway
English
Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior.
Disaster Response
domain adaptation
tweet classification
exported from refbase (http://idl.iscram.org/show.php?record=1234), last updated on Tue, 10 Nov 2015 04:12:30 +0100
text
http://idl.iscram.org/files/hongminli/2015/1234_HongminLi_etal2015.pdf
HongminLi_etal2015
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
ISCRAM 2015
L. Palen
editor
M. Buscher
editor
T. Comes
editor
A. Hughes
editor
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
2015
University of Agder (UiA)
Kristiansand, Norway
conference publication
9788271177881
2411-3387
1