Jorge H. Roman, Linn Marks Collins, Ketan K. Mane, Mark L.B. Martinez, Carolyn E Dunford, & James E. Powell Jr. (2008). Reducing information overload in emergencies by detecting themes in web content. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 101–107). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Information on the Web has become increasingly important in disaster response. Yet much of this information is redundant. We are creating a suite of electronic knowledge management (eKM) tools that can be used to reduce by an order of magnitude the information that people need to read in order to gain and maintain awareness of web content during emergencies. In this paper, we describe research-in-progress on developing these tools and applying them to web content from organizations' websites and individuals' blogs. Results so far indicate that organizations' websites and individuals' blogs provide redundant coverage of general issues and that each provides additional information about specific issues. By using the tools we are developing, responders and victims will be able to quickly gather an overview of general issues derived from many websites, then learn more about specific issues by navigating to a few websites.
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Andrea Zielinski, & Ulrich Bügel. (2012). Multilingual analysis of twitter news in support of mass emergency events. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. © 2012 ISCRAM.
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