Extracting situational awareness from microblogs during disaster events

Abstract

Microblogging sites such as Twitter and Weibo are increasingly being used to enhance situational awareness during various natural and man-made disaster events such as floods, earthquakes, and bomb blasts. During any such event, thousands of microblogs (tweets) are posted in short intervals of time. Typically, only a small fraction of these tweets contribute to situational awareness, while the majority merely reflect the sentiment or opinion of people. Real-time extraction of tweets that contribute to situational awareness is especially important for relief operations when time is critical. However, automatically differentiating such tweets from those that reflect opinion / sentiment is a non-trivial challenge, mainly because of the very small size of tweets and the informal way in which tweets are written (frequent use of emoticons, abbreviations, and so on). This study applies Natural Language Processing (NLP) techniques to address this challenge. We extract low-level syntactic features from the text of tweets, such as the presence of specific types of words and parts-of-speech, to develop a classifier to distinguish between tweets which contribute to situational awareness and tweets which do not. Experiments over tweets related to four diverse disaster events show that the proposed features identify situational awareness tweets with significantly higher accuracy than classifiers based on standard bag-of-words models.