Detection and Categorization of Needs during Crises Based on Twitter Data


  • Pingjing Yang University of Illinois Urbana-Champaign
  • Ly Dinh University of South Florida
  • Alex Stratton University of California Berkeley
  • Jana Diesner Technical University of Munich University of Illinois Urbana-Champaign



The Ukraine-Russia conflict has brought sizable detrimental impact to the global energy, food, finance, and manufacturing industries, as well to many affected people. In this paper, we use Twitter (now X) to automatically identify who needs what from text data and how the types of needs that we categorized and standardize evolved throughout this conflict. Our findings suggest that the Ukraine expresses a need for weapons, Russia for land, Europe for gas, and America for leadership. The majority of needs expressed on Twitter during this conflict are related to the categories transportation, military, health & medical, financial and money, energy, and essential items (food, water, shelter, non-food items). Stated needs changed as the conflict escalated or fell into stalemate. Needs also varied depending on the tweet's location, with tweets from Ukraine's neighboring countries being related to food and medicine, while tweets from non-neighboring countries stated needs for clothing and tents. Tweets written in Ukrainian and Russian shared similar need terms, such as medicines and kits, compared to English tweets, which expressed needs such as ammunition and humanitarian aid. Our comparison of needs across four different disaster events, namely this conflict, an earthquake, a major hurricane, and the COVID-19 pandemic, showed how needs differ depending on the nature of the crisis and how domain-adjustment of needs categories is necessary. We contribute to the crisis informatics literature by (1) validating a methodology for using tweets to study the demand and supply of things that different stakeholders need during crisis events and (2) testing, comparing, and improving the fit of widely used need classification schemas for studying crisis from different domains.




How to Cite

Yang, P., Dinh, L., Stratton, A., & Diesner, J. (2024). Detection and Categorization of Needs during Crises Based on Twitter Data. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 1713-1726.