Abstract
Smart cities are cyber-physical-social systems, where city data from different sources could be collected, processed and analyzed to extract useful knowledge. As the volume of data from the social world is exploding, social media data analysis has become an emerging area in many different disciplines. During crisis events, users may post informative tweets about affected individuals, utility damage or cautions on social media platforms. If such tweets are efficiently and effectively processed and analyzed, city organizations may gain a better situational awareness of the affected citizens and provide better response actions. Advances in deep neural networks have significantly improved the performance in many social media analyzing tasks, e.g., sentiment analysis, fake news detection, crisis data classification, etc. However, deep learning models require a large amount of labeled data for model training, which is impractical to collect, especially at the early stage of a crisis event. To address this limitation, we proposed a BERT-based Adversarial Domain Adaptation model (BERT-ADA) for crisis tweet classification. Our experiments with three real-world crisis datasets demonstrate the advantages of the proposed model over several baselines.