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

TLDR

Mass emergencies generate vast amounts of computer‑mediated communication that are difficult to sift manually, yet contain valuable information that can provide insight into time‑ and safety‑critical situations if captured and analyzed rapidly. The study aims to automatically identify Twitter messages that contribute to situational awareness during mass emergencies. The authors collected tweets from four crisis events, annotated them, and trained a classifier using hand‑annotated and automatically extracted linguistic features to detect situational‑awareness messages. The classifier achieved over 80 % accuracy, generalizes well to similar events, and shows promise for helping the public sift and analyze emergency‑related information.

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.