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Author Zahra Ashktorab; Christopher Brown; Manojit Nandi; Aron Culotta pdf  isbn
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  Title Tweedr: Mining twitter to inform disaster response Type Conference Article
  Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014  
  Volume Issue Pages 354-358  
  Keywords Data mining; Disaster prevention; Disasters; Extraction; Filtration; Information systems; Social networking (online); Classification methods; Disaster response; Extraction phase; Logistic regressions; Natural disasters; Social media; Specific information; Text mining; Emergency services  
  Abstract In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.  
  Address University of Maryland, College Park, United States; University of Texas, Austin, United States; Carnegie Mellon University, United States; Illinois Institute of Technology, United States  
  Corporate Author Thesis  
  Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780692211946 Medium  
  Track Humanitarian Information Systems Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 275  
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Author Cornelia Caragea; Anna Squicciarini; Sam Stehle; Kishore Neppalli; Andrea H. Tapia pdf  isbn
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  Title Mapping moods: Geo-mapped sentiment analysis during hurricane sandy Type Conference Article
  Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014  
  Volume Issue Pages 642-651  
  Keywords Data mining; Disasters; Hurricanes; Information systems; Disaster-related geo-tagged tweets; Online reviews; Online social networkings; Sentiment analysis; Sentiment classification; Social networking sites; Social networking (online)  
  Abstract Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster.  
  Address Computer Science and Engineering, University of North Texas, Denton, TX-76203, United States; Information Sciences and Technology, Pennsylvania State University, University Park, PA-16801, United States  
  Corporate Author Thesis  
  Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780692211946 Medium  
  Track Social Media in Crisis Response and Management Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 372  
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Author Benjamin Herfort; João Porto De Albuquerque; Svend-Jonas Schelhorn; Alexander Zipf pdf  isbn
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  Title Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013 Type Conference Article
  Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014  
  Volume Issue Pages 747-751  
  Keywords Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods  
  Abstract In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.  
  Address GIScience Department, Heidelberg University, Germany; Dept. of Computer Systems/ICMC, University of Sao Paulo, Brazil  
  Corporate Author Thesis  
  Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780692211946 Medium  
  Track Social Media in Crisis Response and Management Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 572  
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Author Benjamin Heuer; Jan Zibuschka; Heiko Roßnagel; Johannes Maucher pdf  isbn
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  Title Empirical analysis of passenger trajectories within an urban transport hub Type Conference Article
  Year 2012 Publication ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2012  
  Volume Issue Pages  
  Keywords Algorithms; Information systems; Trajectories; Urban transportation; Central stations; Data mining algorithm; Empirical analysis; Empirical data; Passenger movements; Simulation framework; Urban transport; Data mining  
  Abstract In this contribution we present an analysis of passenger trajectories in an urban transportation hub. We collected an extensive amount of empirical data consisting of both gate and individual stalking observation in the central station of Cologne. Three different data mining algorithms are used to analyze this data, producing both data that may be used as input for simulation frameworks, and, as an aside, visualizations of passenger movements that could be of high interest to transport and emergency managers. © 2012 ISCRAM.  
  Address Hochschule der Medien (HdM), Germany; Fraunhofer IAO, Germany  
  Corporate Author Thesis  
  Publisher Simon Fraser University Place of Publication Vancouver, BC Editor L. Rothkrantz, J. Ristvej, Z.Franco  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780864913326 Medium  
  Track Analytical Modelling and Simulation Expedition Conference 9th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 129  
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Author Kenneth Joseph; Peter M. Landwehr; Kathleen M. Carley pdf  isbn
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  Title An approach to selecting keywords to track on twitter during a disaster Type Conference Article
  Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014  
  Volume Issue Pages 672-676  
  Keywords Data mining; Disasters; Information systems; Keyword searching; Novel methodology; Situational awareness; Social media; Twitter; Social networking (online)  
  Abstract Several studies on Twitter usage during disasters analyze tweets collected using ad-hoc keywords pre-defined by researchers. While recent efforts have worked to improve this methodology, open questions remain about which keywords can be used to uncover tweets contributing to situational awareness (SA) and the quality of tweets returned using different terms. Herein we consider a novel methodology for uncovering relevant keywords one can use to search for tweets containing situational awareness. We provide a description of the methodology and initial results which suggest our approach may lead to better keywords to use for keyword searching during disasters.  
  Address Carnegie Mellon University, United States  
  Corporate Author Thesis  
  Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780692211946 Medium  
  Track Social Media in Crisis Response and Management Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 640  
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Author Ahmed Nagy; Lusine Mkrtchyan; Klaas Van Der Meer pdf  isbn
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  Title A CBRN detection framework using fuzzy logic Type Conference Article
  Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013  
  Volume Issue Pages 266-271  
  Keywords Data mining; Decision support systems; Disaster prevention; Fuzzy set theory; Information systems; Decision supports; Degree of credibility; Disaster management; Distributed approaches; Evaluation approach; Human activities; Ordered weighted aggregations; Potential values; Fuzzy logic  
  Abstract Identifying a chemical, biological, radiological, and nuclear incident (CBRN) is a challenge. Evidence and health symptoms resulting from CBRN malevolent incident overlap with other normal non malevolent human activities. However, proper fusion of symptoms and evidence can aid in drawing conclusions with a certain degree of credibility about the existence of an incident. There are two types of incidents directly observable, overt, or indirectly observable, covert, which can be detected from the symptoms and consequences. This paper describes a framework for identifying a CBRN incident from available evidence using a fuzzy belief degree distributed approach. We present two approaches for evidence fusion and aggregation; the first, two level cumulative belief degree (CBD) while the second is ordered weighted aggregation of belief degrees (OWA). The evaluation approach undertaken shows the potential value of the two techniques.  
  Address SCK/CEN, Belgium  
  Corporate Author Thesis  
  Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9783923704804 Medium  
  Track Decision Support Systems Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 804  
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Author Alec Pawling; Tim Schoenharl; Ping Yan; Greg Madey pdf  isbn
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  Title WIPER: An emergency response system Type Conference Article
  Year 2008 Publication Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2008  
  Volume Issue Pages 702-710  
  Keywords Data mining; Geographic information systems; Information systems; Agent based simulation; Emergency response; Emergency response systems; Emergency situation; Integrated systems; Running simulations; Simulation systems; Web-based front end; Financial data processing  
  Abstract This paper describes the WIPER system, a proof of concept prototype, and progress made on its development to date. WIPER is intended to provide emergency response managers with an integrated system that detects possible emergencies from cellular communication data, attempts to predict the development of emergency situations, and provides tools for evaluating possible courses of action in dealing with emergency situations. We describe algorithms for detecting anomalies in streaming cellular communication network data, the implementation of a simulation system that validates running simulations with new real world data, and a web-based front end to the WIPER system. We also discuss issues relating to the real-time aggregation of data from the cellular service provider and its distribution to components of the WIPER system.  
  Address Dept. of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46656, United States  
  Corporate Author Thesis  
  Publisher Information Systems for Crisis Response and Management, ISCRAM Place of Publication Washington, DC Editor F. Fiedrich, B. Van de Walle  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780615206974 Medium  
  Track Decentralized and Self-Organizing IT-Infrastructures for Crisis Response and Management Expedition Conference 5th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 836  
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Author Eli Rohn; Gil Erez pdf  isbn
openurl 
  Title Fighting agro-terrorism in cyberspace: A framework for intention detection using overt electronic data sources Type Conference Article
  Year 2012 Publication ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2012  
  Volume Issue Pages  
  Keywords Bioterrorism; Chemical detection; Data mining; Information retrieval; Information systems; Risk assessment; World Wide Web; Authorship; Cyber-terrorism; Digital shadow; Intelligence; Text mining; Terrorism  
  Abstract Agro Terrorism is “a hostile attack, towards an agricultural environment, including infrastructures and processes, in order to significantly damage national and international political interests”. This special session within the early warning track is aimed at reducing agro-terrorism related risks by either means of prevention (intelligence gathering using data mining and chatter mining, for example) or means to response to such an attack by early detection of exotic/foreign pathogenic agents, early prediction of disease dispersion patterns, implementation of biosecurity measures, and the development of future methodologies and techniques related to food defense and post-event response. This paper focuses on intention detection using overt data sources on the World Wide Web as they relate to agro-terrorism threats. The paper focuses on early detection that can lead to prevention of such acts, yet a variety of the techniques presented here are also useful for helping in post-event perpetrators detection. © 2012 ISCRAM.  
  Address Software and Information Systems Engineering Department, Ben Gurion University, Israel; Counter Agro Terrorism Research Center, Israel  
  Corporate Author Thesis  
  Publisher Simon Fraser University Place of Publication Vancouver, BC Editor L. Rothkrantz, J. Ristvej, Z.Franco  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780864913326 Medium  
  Track Planning and Foresight Expedition Conference 9th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 196  
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Author Samuel Lee Toepke pdf  openurl
  Title Temporal Sampling Implications for Crowd Sourced Population Estimations from Social Media Type Conference Article
  Year 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017  
  Volume Issue Pages 564-571  
  Keywords Population estimation; emergency response; temporal sampling; volunteered geospatial information; data mining  
  Abstract Understanding the movements of a population throughout the 24-hour day is critical when directing disaster response in an urban area. An emergency situation can develop rapidly, and understanding the expected locations of groups of people is required for the success of first responders. Recent advances in modern consumer technologies have facilitated the generation, sharing and mining of an extensive amount of volunteered geographic information. Users leverage inexpensive smart devices, pervasive Internet connections and social media services to provide data about geospatial locations. Using an enterprise system, it is possible to aggregate this freely available, geospatially enabled data and create a population estimation with high spatiotemporal resolution, via a heat map. This investigation explores the effects of different temporal sampling periods when creating such estimations. Time periods are selected, estimations are generated for several large urban areas in the western United States, and comparisons of the results are shown/discussed.  
  Address Private Engineering Firm  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN Medium  
  Track Social Media Studies Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management  
  Notes Approved no  
  Call Number Serial 2044  
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Author Schreiber pdf  isbn
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  Title Automatic generation of sensor queries in a WSN for environmental monitoring Type Conference Article
  Year 2007 Publication Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers Abbreviated Journal ISCRAM 2007  
  Volume Issue Pages 245-254  
  Keywords Data mining; Automatic Generation; Data collection; Environmental data; Environmental Monitoring; Physical phenomena; Physical variables; Query generation; Sensor data extraction; Monitoring  
  Abstract The design of a WSN for environmental data monitoring is a largely ad-hoc human process. In this paper, we propose the automatic generation of queries for sensor data extraction, based on the collection of a number of parameters concerning the physical phenomenon to be controlled, the relevant physical variables, the types of sensors to be deployed and their allocation, the data collection frequencies, and other features.  
  Address Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy  
  Corporate Author Thesis  
  Publisher Information Systems for Crisis Response and Management, ISCRAM Place of Publication Delft Editor B. Van de Walle, P. Burghardt, K. Nieuwenhuis  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9789054874171; 9789090218717 Medium  
  Track DSM Expedition Conference 4th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 926  
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Author Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer pdf  isbn
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  Title A fine-grained sentiment analysis approach for detecting crisis related microposts Type Conference Article
  Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013  
  Volume Issue Pages 846-851  
  Keywords Artificial intelligence; Information systems; Learning systems; Risk management; Social networking (online); Amount of information; Emergency management; Microposts; Real-time information; Sentiment analysis; Situational awareness; Systematic evaluation; Twitter; Data mining  
  Abstract Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.  
  Address Technische Universität Darmstadt, Germany; Universität Mannheim, Germany  
  Corporate Author Thesis  
  Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9783923704804 Medium  
  Track Social Media Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 927  
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Author Peter Serwylo; Paul Arbon; Grace Rumantir pdf  isbn
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  Title Predicting patient presentation rates at mass gatherings using machine learning Type Conference Article
  Year 2011 Publication 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 Abbreviated Journal ISCRAM 2011  
  Volume Issue Pages  
  Keywords Artificial intelligence; Data mining; Forecasting; Information systems; Event Types; Heat indices; Machine learning techniques; Mass gathering; Optimization techniques; Predictive models; Predictive variables; Time of day; Learning systems  
  Abstract Mass gatherings have been defined as events where more than 1,000 people are present for a defined period of time. Such an event presents specific challenges with respect to medical care. First aid is provisioned on-site at most events in order to prevent undue strain on the local emergency services. In order to allocate enough resources to deal with the expected injuries, it is important to be able to accurately predict patient volumes. This study used machine learning techniques to identify which variables are the most important in predicting patient volumes at mass gatherings. Data from 201 mass gatherings across Australia was analysed, finding that event type is the most predictive variable, followed by the state or territory, heat index, humidity, whether it is bounded, and the time of day. Variables with little bearing on the outcome included the presence of alcohol, whether the event was indoors or outdoors, and whether it had one point of focus. The best predictive models produced acceptable predictions of the patient presentations 80% of the time, and this could be further improved using optimization techniques.  
  Address Monash University, Australia; Flinders University, Australia  
  Corporate Author Thesis  
  Publisher Information Systems for Crisis Response and Management, ISCRAM Place of Publication Lisbon Editor M.A. Santos, L. Sousa, E. Portela  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9789724922478 Medium  
  Track Planning and Foresight Expedition Conference 8th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 938  
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Author Gayane Shalunts; Gerhard Backfried; Prinz Prinz pdf  isbn
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  Title Sentiment analysis of German social media data for natural disasters Type Conference Article
  Year 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014  
  Volume Issue Pages 752-756  
  Keywords Disasters; Information systems; First responders; Integral part; Media analysis; Multiple languages; Natural disasters; Sentiment analysis; Social media; Social media datum; Data mining  
  Abstract Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis, particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new manner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013.  
  Address SAIL LABS Technology AG, Vienna, Austria  
  Corporate Author Thesis  
  Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9780692211946 Medium  
  Track Social Media in Crisis Response and Management Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 940  
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Author Yan, S. pdf  isbn
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  Title Design of enterprise crisis predicting system based on cluster and outlier data mining Type Conference Article
  Year 2005 Publication Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2005  
  Volume Issue Pages 143-145  
  Keywords Forecasting; Industry; Information systems; Statistics; Cluster; Crisis predicting system; Frame construction; Working process; Data mining  
  Abstract In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on cluster and outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it is a new way to solve such problems.  
  Address School of Economics and Management, Harbin Engineering University, 150001, China  
  Corporate Author Thesis  
  Publisher Royal Flemish Academy of Belgium Place of Publication Brussels Editor B. Van de Walle, B. Carle  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9076971099 Medium  
  Track POSTER SESSION Expedition Conference 2nd International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 1117  
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Author Andrea Zielinski; Stuart E. Middleton; Laurissa N. Tokarchuk; Xinyue Wang pdf  isbn
openurl 
  Title Social media text mining and network analysis for decision support in natural crisis management Type Conference Article
  Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013  
  Volume Issue Pages 840-845  
  Keywords Arts computing; Decision support systems; Information systems; Software prototyping; Decision supports; Link analysis; Social media; Text mining; Vgi; Web Mining; Data mining  
  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.  
  Address Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany; IT Innovation Centre, University of Southampton, Southampton, United Kingdom; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom  
  Corporate Author Thesis  
  Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9783923704804 Medium  
  Track Social Media Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 1160  
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