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Briony Gray, Mark J. Weal, & David Martin. (2017). Social Media during a Sustained Period of Crisis: The Case of the UK Storms. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 633–644). Albi, France: Iscram.
Abstract: This paper analyses the social media communications surrounding the 2015 – 2016 series of winter storms in the UK. Three storms were selected for analysis over a sustained period of time; these were storms Desmond, Eva and Frank which made landfall within quick succession of one another. In this case study we examine communications relating to multiple hazards which include flooding, evacuation and weather warnings using mainstream media content such as news stories, and online content such as Twitter data. Using a mixed method approach of content analysis combined with the application of a conceptual framework, we present (i.) the network of emergency responders managing events, (ii.) an analysis of crisis communications over time, and (iii.) highlight the barriers posed to effective social media communications during multi-hazard disasters. We conclude by assessing how these barriers may be lessened during prolonged periods of crisis.
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Starr Roxanne Hiltz, & Linda Plotnick. (2013). Dealing with information overload when using social media for emergency management: Emerging solutions. 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. 823–827). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Several recent studies point the way to enabling emergency response managers to be able to find relevant posts and incorporate them into their sensemaking and decision making processes. Among the approaches that have improved the ability to find the most relevant information are the social conventions of creating topic groups and tags and of “retweeting;” the use of trained volunteers to filter and summarize posts for responders; automated notifications of trending topics; natural language processing of posts; techniques for identifying posts from the disaster site; and the use of GIS and crisis maps to visually represent the distribution of incidents.
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Geoffrey Hoare, Jeffrey Nield, Tom Belcuore, & Tom Rich. (2008). Information needs and decision support in health and medical disasters. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 778–786). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: During a disaster, health and medical decision makers need accurate, timely information. However, it is seldom readily available to the right decision makers, at the right time. Quite a number of databases currently exist with information about health and medical organizations which decision makers need during a disaster. Some of these databases have functions that facilitate decision-making and communication before, during and after a disaster. In theory, linking several existing databases will supply this information. Also, other functions can be provided in one package for incident management and monitoring of the preparedness capacity of a State's health and medical systems. But, this has not happened yet in Florida. This research assessed the different users needs, defined the information required to make good decisions and is testing a pilot decision support system of linked databases.
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Erik R. Janus, Susan Manente, & Sharon L. Lee. (2008). Best practices in chemical emergency risk communication: The Interstate Chemical Terrorism Workgroup. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 774–777). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The Interstate Chemical Terrorism Workgroup (ICTW) was formed in 2002 and currently includes members from nearly all states and Washington, DC, as well as representation from a number of non-governmental organizations. In addition to offering monthly conference call/presentations, the ICTW partnered with the Centers for Disease Control and Prevention (CDC) in 2003 to host a workshop to address basic elements of risk communication needs in a chemical event. The primary goal of the workshop was to develop a list of core competencies and benchmarks as well as a series of fact sheet templates destined for the general public and press, health care providers, public health department and/or officials, and first response and emergency workers (Lee et al., 2006). Key findings of the 2003 workshop, along with other work being done by CDC, academia and the states, underscore the importance of public health agencies in providing risk communication services during (and particularly after) chemical emergencies, whether intentional or not. Tools developed by the ICTW have been used and/or consulted by many groups involved in public health preparedness. This case study will examine the efforts of Michigan to implement these tools to reduce information overload in an emergency.
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Jens Kersten, Jan Bongard, & Friederike Klan. (2021). Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 744–754). Blacksburg, VA (USA): Virginia Tech.
Abstract: Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses.
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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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Simon Tucker, Vitaveska Lanfranchi, Neil Ireson, Alfonso Sosa, Gregoire Burel, & Fabio Ciravegna. (2012). Straight to the information I need: Assessing collational interfaces for emergency response. 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: Collational interfaces gather information from a range of sources and present them to users. Information overload is tackled by processing information in the back-end and providing interactive means to filter and browse data. Such interfaces have applications in emergency response – giving users the right information to act effectively. In this paper we explore a collational interface for emergency response, carrying out a user study that compares it to a paper based interface and one which presents data without collating it. We demonstrate that a collational interface allows users to build a picture of an emergency, but not necessarily in less time. © 2012 ISCRAM.
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Maarten Van Someren, Niels Netten, Vanessa Evers, Henriette Cramer, Robert De Hoog, & Guido Bruinsma. (2005). A trainable information distribution system to support crisis management. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 203–206). Brussels: Royal Flemish Academy of Belgium.
Abstract: Crisis response and management involve multiple collaborative actors who execute tasks in a dynamic setting. For the effectiveness of collaboration and crisis fighting it is essential that all actors have access to relevant information necessary for their tasks. Managing the information flow, i.e. presenting the right information to the right person at the right time, is of great importance. However, the complexity of a crisis event makes it very difficult to keep an overview of all ongoing activities and information flow within the entire crisis environment. In this paper we address the problem of selecting and distributing information to users as a function of their characteristics, tasks and the state of their workflows in a collaborative setting. In particular, we propose a trainable system for information distribution that will be able to support the dynamic nature of collaborative processes and provide users with task-relevant information. We expect that this will reduce problems due to information overload and will lead to more effective collaboration between all actors in the crisis management environment.
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