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https://doi.org/10.1145/315289...
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Filtering informative tweets during emergencies

a machine learning approach
Authors: Acerbo, Flavia Sofia; Rossi, Claudio;

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.

© 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

Subjects by Vocabulary

Microsoft Academic Graph classification: Emergency management Computer science business.industry computer.software_genre Machine learning Emergency situations Information extraction Social media Artificial intelligence business Natural disaster computer Classifier (UML)

Keywords

Information extraction, Applied computing, Computing methodologies, Machine learning

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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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downloads
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