CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

Henry Peng Zou; Yue Zhou; Cornelia Caragea; Doina Caragea

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arXiv · 2310.14627

CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

Henry Peng Zou·Yue Zhou·Cornelia Caragea·Doina Caragea

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Abstract

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.

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Henry Peng Zou, Yue Zhou, Cornelia Caragea, Doina Caragea. 2023-10-23. CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification. https://arxiv.org/abs/2310.14627

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