Abstract
Increasingly, more important information is being shared through Twitter. New opportunities arise to use this tool to detect emergencies and extract crucial information about the scope and nature of that event. A major challenge for the extraction of emergency event information from Twitter is represented by the unstructured and noisy nature of tweets. Within the SABESS project we propose a combined structural and content based analysis approach. We use social network analysis to identify reliable tweets and content analysis techniques to summarize key emergency facts.
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Klein, B., Laiseca, X., Casado-Mansilla, D., López-de-Ipiña, D., Nespral, A.P. (2012). Detection and Extracting of Emergency Knowledge from Twitter Streams. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_64
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DOI: https://doi.org/10.1007/978-3-642-35377-2_64
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-35377-2
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