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Abstract: In this paper we present a novel approach to reduce information proliferation and aid better information structure by automatically generating extraction of abbreviation for emergency management websites. 5.7 Giga Byte web data from 624 emergency management related web sites is collected and a list of acronyms is automatically generated by proposed system (AbbrevExtractor). Being the first attempt of applying abbreviation extraction to the field, this work is expected to provide comprehensive and timely information for emergency management communities in emergency preparedness, training and education. Future work is likely to involve more data collection and intelligent text analysis for dynamically maintaining and updating the list of acronyms and abbreviations.

Keywords: Civil defense; Disasters; Extraction; Information systems; Risk management; Websites; Acronyms and abbreviations; Automatically generated; Emergency management; Emergency preparedness; Information structures; Text analysis; Training and education; Web Mining; Information management

Track: Methods for Mitigating Information Overload

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Abstract: A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus.

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	Min Song, & Peishih Chang. (2008). Automatic extraction of abbreviation for emergency management websites. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 93–100). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM. Abstract: In this paper we present a novel approach to reduce information proliferation and aid better information structure by automatically generating extraction of abbreviation for emergency management websites. 5.7 Giga Byte web data from 624 emergency management related web sites is collected and a list of acronyms is automatically generated by proposed system (AbbrevExtractor). Being the first attempt of applying abbreviation extraction to the field, this work is expected to provide comprehensive and timely information for emergency management communities in emergency preparedness, training and education. Future work is likely to involve more data collection and intelligent text analysis for dynamically maintaining and updating the list of acronyms and abbreviations. Keywords: Civil defense; Disasters; Extraction; Information systems; Risk management; Websites; Acronyms and abbreviations; Automatically generated; Emergency management; Emergency preparedness; Information structures; Text analysis; Training and education; Web Mining; Information management Track: Methods for Mitigating Information Overload Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML
	Andrea Zielinski, Stuart E. Middleton, Laurissa N. Tokarchuk, & Xinyue Wang. (2013). Social media text mining and network analysis for decision support in natural crisis management. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 840–845). KIT; Baden-Baden: Karlsruher Institut fur Technologie. Abstract: A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus. Keywords: Arts computing; Decision support systems; Information systems; Software prototyping; Decision supports; Link analysis; Social media; Text mining; Vgi; Web Mining; Data mining Track: Social Media Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML