Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management


  • Wenlin Yao Texas A&M University
  • Cheng Zhang Texas A&M University
  • Shiva Saravanan Princeton University
  • Ruihong Huang Texas A&M University
  • Ali Mostafavi Texas A&M University




People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.




How to Cite

Yao, W., Zhang, C., Saravanan, S., Huang, R., & Mostafavi, A. (2020). Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 532-539. https://doi.org/10.1609/aaai.v34i01.5391



AAAI Special Technical Track: AI for Social Impact