  The shared real-time information about natural disasters on social media
platforms like Twitter and Facebook plays a critical role in informing
volunteers, emergency managers, and response organizations. However, supervised
learning models for monitoring disaster events require large amounts of
annotated data, making them unrealistic for real-time use in disaster events.
To address this challenge, we present a fine-grained disaster tweet
classification model under the semi-supervised, few-shot learning setting where
only a small number of annotated data is required. Our model, CrisisMatch,
effectively classifies tweets into fine-grained classes of interest using few
labeled data and large amounts of unlabeled data, mimicking the early stage of
a disaster. Through integrating effective semi-supervised learning ideas and
incorporating TextMixUp, CrisisMatch achieves performance improvement on two
disaster datasets of 11.2\% on average. Further analyses are also provided for
the influence of the number of labeled data and out-of-domain results.
