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Rapid crisis response requires real-time analysis of messages. After adisaster happens, volunteers attempt to classify tweets to determine needs,e.g., supplies, infrastructure damage, etc. Given labeled data, supervisedmachine learning can help classify these messages. Scarcity of labeled datacauses poor performance in machine training. Can we reuse old tweets to trainclassifiers? How can we choose labeled tweets for training? Specifically, westudy the usefulness of labeled data of past events. Do labeled tweets indifferent language help? We observe the performance of our classifiers trainedusing different combinations of training sets obtained from past disasters. Weperform extensive experimentation on real crisis datasets and show that thepast labels are useful when both source and target events are of the same type(e.g. both earthquakes). For similar languages (e.g., Italian and Spanish),cross-language domain adaptation was useful, however, when for differentlanguages (e.g., Italian and English), the performance decreased.

Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages