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During natural or man-made disasters, humanitarian response organizationslook for useful information to support their decision-making processes. Socialmedia platforms such as Twitter have been considered as a vital source ofuseful information for disaster response and management. Despite advances innatural language processing techniques, processing short and informal Twittermessages is a challenging task. In this paper, we propose to use Deep NeuralNetwork (DNN) to address two types of information needs of responseorganizations: 1) identifying informative tweets and 2) classifying them intotopical classes. DNNs use distributed representation of words and learn therepresentation as well as higher level features automatically for theclassification task. We propose a new online algorithm based on stochasticgradient descent to train DNNs in an online fashion during disaster situations.We test our models using a crisis-related real-world Twitter dataset.

Applications of Online Deep Learning for Crisis Response Using Social Media Information