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During time-critical situations such as natural disasters, rapidclassification of data posted on social networks by affected people is usefulfor humanitarian organizations to gain situational awareness and to planresponse efforts. However, the scarcity of labeled data in the early hours of acrisis hinders machine learning tasks thus delays crisis response. In thiswork, we propose to use an inductive semi-supervised technique to utilizeunlabeled data, which is often abundant at the onset of a crisis event, alongwith fewer labeled data. Specif- ically, we adopt a graph-based deep learningframework to learn an inductive semi-supervised model. We use two real-worldcrisis datasets from Twitter to evaluate the proposed approach. Our resultsshow significant improvements using unlabeled data as compared to only usinglabeled data.

Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets