PanTop: Pandemic Topic Detection and Monitoring System (Student Abstract)


  • Yangxiao Bai South Dakota State University
  • Kaiqun Fu South Dakota State University



Topic Modeling, Social Media, Covid-19, Spatial Data Mining, Storyline


Diverse efforts to combat the COVID-19 pandemic have continued throughout the past two years. Governments have announced plans for unprecedentedly rapid vaccine development, quarantine measures, and economic revitalization. They contribute to a more effective pandemic response by determining the precise opinions of individuals regarding these mitigation measures. In this paper, we propose a deep learning-based topic monitoring and storyline extraction system for COVID-19 that is capable of analyzing public sentiment and pandemic trends. The proposed method is able to retrieve Twitter data related to COVID-19 and conduct spatiotemporal analysis. Furthermore, a deep learning component of the system provides monitoring and modeling capabilities for topics based on advanced natural language processing models. A variety of visualization methods are applied to the project to show the distribution of each topic. Our proposed system accurately reflects how public reactions change over time along with pandemic topics.




How to Cite

Bai, Y., & Fu, K. (2023). PanTop: Pandemic Topic Detection and Monitoring System (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16158-16159.