Conference paper Open Access

Filtering Informative Tweets during Emergencies: A Machine Learning Approach

Acerbo, Flavia Sofia; Rossi, Claudio

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.

© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Flavia Sofia Acerbo and Claudio Rossi. 2017. Filtering Informative Tweets during Emergencies: A Machine Learning Approach. In Proceedings of I-TENDER '17, Incheon, Republic of Korea, December 12, 2017, 6 pages. https://doi.org/10.1145/3152896.3152897
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