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Laura Petersen, Eva Horvath, & Johan Sjöström. (2019). Evaluating Critical Infrastructure Resilience via Tolerance Triangles: Hungarian Highway pilot case study. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 1210–1225). Valencia, Spain: Iscram.
Abstract: While accepted as part of critical infrastructure (CI) resilience, no consensus exists on how to measure the exact minimum level of service or the rapidity of rapidly restoring services. The H2020 European project IMPROVER (Improved risk evaluation and implementation of resilience concepts to critical infrastructure) suggests to use the public’s declared tolerance levels for both minimum level of service and rapidity of service restoration as criteria with which to evaluate if the resilience of a given CI is resilient enough. This paper demonstrates the development of a questionnaire-based methodology to determine public tolerance levels. It then tests this methodology via a pilot case study at IMPROVER’s Hungarian Highway Living Lab. The paper demonstrates that 1) the questionnaire-based methodology permits one to evaluate public perception in such a way as to compare it to technical resilience analyses and 2) public tolerance levels are a reasonable choice for a criteria for resilience evaluation.
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Xiaodan Yu, & Deepak Khazanchi. (2019). The Influence of Swift Trust on Virtual Team’s Sensemaking in Crisis: A Research Model. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 1330–1336). Valencia, Spain: Iscram.
Abstract: Virtual teams are an important form of collaboration, especially in the context of transboundary organizational crises. Achieving team effectiveness through good sensemaking is key to virtual teams’ success in responding to crisis. However, there is still a lack of understanding about the relationship of this sensemaking in a virtual team during crisis to the virtual team’s development of swift trust. Adapting from a model of sensemaking, in this paper, we propose a research model to describe the relationships among swift trust, sensemaking and team performance in the context of virtual teams during crisis.
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Jun Sasaki, Taeko Maejima, Shuang Li, Yusuke Sato, & Minoru Hiyama. (2019). Life-Area Broadcasting System (LABS) for Usual and Emergency Cases by Using Easy Contents Management System and New Speaker Devices. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 933–939). Valencia, Spain: Iscram.
Abstract: The “community” has played an important role in enhancing the regional disaster management capabilities in Japan. This paper proposes a Life-Area Broadcasting System (LABS) for usual and emergency cases. In order to realize very simple and easy management of LABS, we developed the Easy Contents Management System. By this system, people can obtain life-area information related to their life support, small events and accident news occurring at their living area not only in emergency cases but also in usual cases by voice and images. Further, we developed some new Speaker Devices for unfamiliar users of ICT devices such as elderly users. Those users can receive the service of LABS as like as a television or a radio broadcast terminal anytime and every day. In this paper, we describe the concept of LABS and introduces the developed new systems and devices.
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Asmelash Teka Hadgu, Sallam Abualhaija, & Claudia Niederée. (2019). Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 923–931). Valencia, Spain: Iscram.
Abstract: When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in order to exchange information or to share emotions. These posts can provide useful information to raise situation awareness and support decision making. In this paper, we propose a novel method for social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time and uses a counter-stream for keeping the inclusion of noise and irrelevant information under control. In addition, we contribute to another still under-developed area for adaptive crawling, namely systematic evaluation of recall. We come up with a framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a semi-automated approach for ground-truth construction. We show that our method outperforms previous methods for very large scale events.
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Jess Kropczynski, Rob Grace, Shane Halse, Doina Caragea, Cornelia Caragea, & Andrea Tapia. (2019). Refining a Coding Scheme to Identify Actionable Information on Social Media. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 916–922). Valencia, Spain: Iscram.
Abstract: This paper describes the use of a previously established qualitative coding scheme developed through a design workshop with public safety professionals, and applied the schema to social media data collecting during crises. The intention of applying this scheme to existing crisis datasets was to acquire training data for machine learning. Applying the coding scheme to social media data revealed that additional subcategories of the coding scheme are necessary to satisfy information requirements necessary to dispatch first responders to an incident. The coding scheme was refined and adapted into a set of instructions for qualitative coders on Amazon Mechanical Turk. The contribution of this work is a coding scheme that is more directly related to the information needs of public safety professionals. Implications of early results using the refined coding scheme are discussed in terms of proposed automated methods to identify actionable information for dispatch of first responders during emergency incidents.
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