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Domain adaptation methods have been introduced for auto-filtering disaster tweets to address the issue of lacking labeled data for an emerging disaster. In this article, the authors present and compare two simple, yet effective approaches for the task of classifying disaster-related tweets. The first approach leverages the unlabeled target disaster data to align the source disaster distribution to the target distribution, and, subsequently, learns a supervised classifier from the modified source data. The second approach uses the strategy of self-training to iteratively label the available unlabeled target data, and then builds a classifier as a weighted combination of source and target-specific classifiers. Experimental results using Naïve Bayes as the base classifier show that both approaches generally improve performance as compared to baseline. Overall, the self-training approach gives better results than the alignment-based approach. Furthermore, combining correlation alignment with self-training leads to better result, but the results of self-training are still better.