Abstract

Social media has become an important medium for the release and dissemination of disaster information, the effective identification and utilization of which is of great significance to disaster emergency management. Given the shortcomings of the traditional text classification model, a disaster tweet classification method was proposed based on the pre-trained model of bidirectional encoder representations from transformers (BERT). After data cleaning and preprocessing, this study constructed a text classification model based on long short-term memory-convolutional neural network (LSTM-CNN) through comparative analysis, based on BERT. Experiments on the tweet datasets of the Kaggle competition platform showed that the proposed classification model outperforms the traditional Naive Bayesian classification model and the common fine-tuning model, with the recognition rate up to 85%. This study could shed significant light on enhancing the identification accuracy of real disaster information and the efficiency of disaster emergency response.

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The authors are grateful for the financial support received from the National Natural Science Foundation of China (No. 72091512, No. 51908323).

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