tweets

detection sars-cov

media data

wind_132264

aegypti_113594

information dissemination

psa levels

microblog

egalitarian

disaster_82128

organic matter dom

semantic relations

posted

pedagogy_133928

repetitive behaviors

vaccination intention

storyline

fetus_77894

disaster_1432

disaster prevention

ISCRAM

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.

Tweedr: Mining twitter to inform disaster response.