In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.


  author =       { Zahra Ashktorab and Christopher Brown and Manojit Nandi and Aron Culotta},
  title =        {Tweedr: {M}ining {T}witter to Inform Disaster Response},
  booktitle = {Proceedings of the 11th International ISCRAM Conference},
  year =         2014