Mahshid Marbouti, Craig Anslow, & Frank Maurer. (2018). Evaluation results for a Social Media Analyst Responding Tool. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 480–492). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: We take a human-centered design approach to develop a fully functional prototype, SMART (“Social Media Analyst Responding Tool”), informed by emergency practitioners. The prototype incorporates machine learning techniques to identify relevant information during emergencies. In this paper, we report the result of a user study to gather qualitative feedback on SMART. The evaluation results offer recommendations into the design of Social Media analysis tools for emergencies. The evaluation findings show the interest of emergency practitioners into designing such solutions; it reflects their need to not only identify relevant information but also to further perceive the outcome of their actions in social media. We found out there is a notable emphasis on the sentiment from these practitioners and social media analysis tools need to do a better job of handling negative sentiment within the emergency concept.
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Audrey Fertier, Aurélie Montarnal, Sébastien Truptil, Anne-Marie Barthe-Delanoë, & Frédérick Bénaben. (2017). A situation model to support collaboration and decision-making inside crisis cells, in real time. 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. 1020–1028). Albi, France: Iscram.
Abstract: Natural and man-made hazards have many unexpected consequences that concern as many heterogeneous services. The GéNéPi project offers to support officials in addressing those events: its purpose is to support the collaboration in the field and the decision-making in the crisis cells. To succeed, the GéNéPi system needs to be aware of the ongoing crisis developments. For now, its best chance is to benefit from the ever growing number of available data sources. One of its goals is, therefore, to learn how to manage numerous, heterogeneous, more or less reliable data, in order to interpret them, in time, for the officials. The result consists on a situation model in the shape of a common operational picture. This paper describes every stage of modelling from the raw data selection, to the use of the situation model itself.
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Grégoire Burel, Lara S. G. Piccolo, Kenny Meesters, & Harith Alani. (2017). DoRES -- A Three-tier Ontology for Modelling Crises in the Digital Age. 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. 834–845). Albi, France: Iscram.
Abstract: During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although dierent models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 dierent competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 dierent competency questions to the classes, properties and relations of the implemented ontology.
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Mahshid Marbouti, Irene Mayor, Dianna Yim, & Frank Maurer. (2017). Social Media Analyst Responding Tool: A Visual Analytics Prototype to Identify Relevant Tweets in Emergency Events. 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. 572–582). Albi, France: Iscram.
Abstract: Public and humanitarian organizations monitor social media to extract useful information during emergencies. In this paper, we propose a new method for identifying situation awareness (SA) tweets for emergencies. We take a human centered design approach to developing a visual analytics prototype, SMA-RT (“Social Media Analyst Responding Tool”), informed by social media analysts and emergency practitioners. Our design offers insights into the main requirements of social media monitoring tools used for emergency purposes. It also highlights the role that human and technology can play together in such solutions. We embed a machine learning classifier to identify SA tweets in a visual interactive tool. Our classifier aggregates textual, social, location, and tone based features to increase precision and recall of SA tweets.
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Samer Cheade, Nada Matta, Jean-Baptiste Pothin, & Remi Cogranne. (2019). Situation Representation and Awareness for Rescue Operations. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: During rescue operations, being aware of the situation is very critical for rescuers and decision-makers to reduce the impacts. This work aims to support situation awareness amongst actors participating in rescue operations by adopting an ontology-based approach. An application ontology is proposed based on existing related ontologies and operational expertise collection. It will help to ensure common situation representation and understanding between different actors. After that, a knowledge-based system will be developed and integrated in actors’ environment to support decision-making. Our preliminary results are shown in this paper.
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