Filtering informative tweets during emergencies

Abstract

Thanks to their worldwide extension and speed, online social networks have become a common and effective way of communication throughout emergencies. The messages posted during a disaster may be either crisis-relevant (alerts, help requests, damage descriptions, etc.) or not (feelings, opinions, etc.) In this paper, we propose a machine learning approach for creating a classifier able to distinguish between informative and not informative messages, and to understand common patterns inside these two classes. We also investigate similarities and differences in the words that mostly occur across three different natural disasters: fire, earthquake and floods. The results, obtained with real data extracted from Twitter during past emergency events, demonstrate the viability of our approach in providing a filtering service able to deliver only informative contents to crisis managers in a view of improving the operational picture during emergency situations.

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