Jill L. Drury, Amanda Anganes, Heather Byrne, Maria C. Casipe, Roger Dejean, Simone Hill, et al. (2012). Badge-primed decision making. 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: We have been investigating new decision support methods for emergency responders. Most recently, we have added to our decision support prototype the concept of “badges”: symbols that cue decision makers to the top-ranked option(s) that are the recommended alternatives for a particular decision. This paper provides the rationale for badges, a description of the initial implementation, results from our first experiment with badges, and a discussion of the next steps. As a report on work-in-progress, the primary contribution of this paper is the description of the concept of badges and its proposed use for emergency response decision making. © 2012 ISCRAM.
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Kiran Zahra, Rahul Deb Das, Frank O. Ostermann, & Ross S. Purves. (2022). Towards an Automated Information Extraction Model from Twitter Threads during Disasters. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 637–653). Tarbes, France.
Abstract: Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes.
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Bogdan Tatomir, & Leon J.M. Rothkrantz. (2005). Crisis management using mobile ad-hoc wireless networks. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 147–149). Brussels: Royal Flemish Academy of Belgium.
Abstract: In this paper we describe a disaster management system. It is assumed that each individual in the field is equipped with a PDA (Personal Digital Assistant) and that can communicate with other PDAs in the vicinity. Together the PDAs form an ad-hoc network. Users can enter their own observations to the PDA, like the position of victims, or a description of the current situation at particular location (e.g. smoke, emergency exits, traffic congestion). This information is entered in a special iconic language. Reversibly, the PDAs inform the users on the overall current situation of the crisis. In order to come to a shared view of the world, the knowledge that is present in the network has to be shared and fused. The proposed way to communicate is via a shared blackboard. This approach facilitates communication in a time and place independent way.
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