Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency

Authors

  • Sudha Verma University of Colorado
  • Sarah Vieweg University of Colorado
  • William Corvey University of Colorado
  • Leysia Palen University of Colorado
  • James Martin University of Colorado
  • Martha Palmer University of Colorado
  • Aaron Schram University of Colorado
  • Kenneth Anderson University of Colorado

Abstract

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies.

We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.

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Published

2021-08-03

How to Cite

Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., & Anderson, K. (2021). Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 385-392. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14119