Adversarial Domain Adaptation for Crisis Data Classification on Social Media

Qi Chen, Wei Wang, Kaizhu Huang, Suparna De, Frans Coenen

Research output: Chapter in Book/Report/Conference proceedingPaper published in a conference proceedingspeer-review


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.
Original languageEnglish
Title of host publication2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
PublisherIEEE Computer Society
Publication statusPublished - 2 Nov 2020


  • Adversarial domain adaptation
  • Smart city
  • Natural language processing
  • BERT
  • Crisis response

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