Disaster response aided by tweet classification with a domain adaptation approach

Social media platforms such as Twitter provide valuable information for aiding disaster response during emergency events. Machine learning could be used to identify such information. However, supervised learning algorithms rely on labelled data, which is not readily available for an emerging target disaster. While labelled data might be available for a prior source disaster, supervised classifiers learned only from the source disaster may not perform well on the target disaster, as each event has unique characteristics (e.g., type, location, and culture) and may cause different social media responses. To address this limitation, we propose to use a domain adaptation approach, which learns classifiers from unlabelled target data, in addition to source labelled data. Our approach uses the Naive Bayes classifier, together with an iterative Self-Training strategy. Experimental results on the task of identifying tweets relevant to a disaster of interest show that the domain adaptation classifiers are better as compared to the supervised classifiers learned only from labelled source data.