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Frâncila Weidt Neiva, & Marcos R. S. Borges. (2019). Sharing Gut Feelings to Support Collaborative Decision Making. 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: Expertise-based intuition plays a key role in decision making, especially in complex environments as those
involved with crisis and emergency domains where decisions often need to be made on the basis of dynamic,
incomplete, and/ or contradictory information. In such environments, a deliberative analysis is often impossible
or inefficient. Using teams to make collaborative decisions in complex environments can bring benefits to
organizations, but the complexity of supporting this scenario also increases. The present work proposes a
solution based on graphs to support the sharing of the intuition rationale in teams aiming at an accelerated
expertise. The development of the proposal is part of a methodological context of design science research. In
this paper we report the execution of one of the expected cycles that explores the use of generated artifacts in
practice that then produced insights for the proposed computational support.
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Francisco J. Quesada Real, Fiona McNeill, Gábor Bella, & Alan Bundy. (2017). Improving Dynamic Information Exchange in Emergency Response Scenarios. 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. 824–833). Albi, France: Iscram.
Abstract: Emergency response scenarios are characterized by the participation of multiple agencies, which cooperate to control the situation and restore normality. These agencies can come from diverse areas of expertise which entails that they represent knowledge dierently, using their own vocabularies and terminologies. This fact complicates the automation of the information-sharing process, creating problems such as ambiguity or specialisation. In this paper we present an approach to tackle these problems by domain-aware semantic matching. This method requires the formalisation of domain-specific terminologies which will be added to an existing system oriented to emergency response. Concretely, we have formalised terms from the UK Civil and Protection Terminology lexicon, which gathers some of the most common terms that UK agencies use in these scenarios.
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Francisco José Quesada Real, Fiona McNeill, Gábor Bella, & Alan Bundy. (2018). Identifying Semantic Domains in Emergency Scenarios. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1130–1132). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Emergency scenarios are characterised by the participation of multiple and diverse organisations which come from different areas. This diversity is enriching in terms of expertise and approaches to tackle problems, however, it also provokes misunderstandings caused by semantic interoperability problems. There are some approaches which propose tackling these problems by using domain adaptation algorithms. Nevertheless, it is not trivial their application in emergency scenarios where the term “domain” is used in many different ways, not being clear either what it means or which domains are involved in these scenarios. In this paper, we identify semantic domains involved in emergency scenarios by analysing papers published in proceedings of ISCRAM and ISCRAM-med conferences. As a result, a categorisation of these domains has been developed, with the aim of providing a resource that can be used by domain adaptation algorithms to tackle problems such as those involving semantic interoperability.
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Franco, Z., Baker, N., R. Okusanya, T., Haque, M. R., Gresser, J., Rubya, S., et al. (2023). Customizing the BattlePeer App: Connecting First Responders with Peer Support to Manage Mental Health Crises. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 272–283). Omaha, USA: University of Nebraska at Omaha.
Abstract: The prevalence and severity of mental health disorders are high among first responders. Routine exposure to trauma, unique work patterns and the social stigma of seeking care exacerbate their challenges. While there are many mHealth applications for effective interventions, they primarily focus on support, education, and symptom identification and management. Our research uses empirical data to inform the customization of the BattlePeer application, previously tested among US veterans. Through focus groups with first responders, we identify specific barriers to help in this population. Our work highlights the potential benefits of adapting an app to create effective peer support strategies. We suggest the modification of BattlePeer to help first responders meet their mental health needs through peer support with tailored feedback and notifications. This will help negotiate the pervasive social isolation and hesitance in articulating emotions described in focus groups that lend to negative mental health outcomes.
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FREALLE Noémie, TENA-CHOLLET Florian, & SAUVAGNARGUES Sophie. (2017). The key role of animation in the execution of crisis management exercises. 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. 916–928). Albi, France: Iscram.
Abstract: The organizers of crisis management exercises want scenario credible and pedagogical from the beginning until the end. For this reason, they call on an animation team that can use different communication channels. The aim of this article is to understand the different types of animation by analyzing the professional experience of the facilitators and the type of casting that can be done. Finally, a definition of four levels of animation is proposed. These levels are associated with different types of messages and rhythm settings. The main objective is to improve the execution of the scenario during a crisis management training.
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Frederick Benaben, & Lysiane Benaben. (2020). Science Fiction: Past and Future Trends of Crisis Management. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1130–1139). Blacksburg, VA (USA): Virginia Tech.
Abstract: This paper is a position paper, presenting an original but very anticipative and mainly imaginative vision of the evolution of the crisis management domain. After analyzing the options to make the past evolutions of that domain somehow explainable (mainly by analyzing the data of all the articles of the last fifteen editions of the ISCRAM conference), the paper aims at providing a framework to assess and evaluate the maturity of the domain of crisis management. Moreover, this framework is also used to tentatively infer some future evolutions and some directions that could be relevant, dangerous, tricky or of great benefit for the crisis management domain. These future trends are mainly based on the current maturity of crisis management (according to the proposed framework) and current or future influential practices, technologies or threats. It will be necessary to wait for fifteen years to see if these bets should be considered as accurate.
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G.P. Jayasiri, & Raj Prasanna. (2023). Citizen Science for supporting Disaster Management Institutions in Sri Lanka. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 77–88). Palmerston North, New Zealand: Massey Unversity.
Abstract: During 2016, 2017 and 2018, the country witnessed extreme rains which triggered flooding in several urban areas. The number of affected people by the 2018 floods was around 150,000 which shows a significant decrease compared to the events in 2016 and 2017. Several institutions provided their support via funding, relief, and rehabilitation mechanisms during these consecutive disasters. However, there are provisions which can further improve the performance of Disaster Management activities. Given this context, this study is carried out to investigate the application of citizen science concepts in several phases of Disaster Management in Sri Lanka. A scoping review supported by three case studies of floods was considered during the analysis. Limited participation of grass root level communities in decision-making and disaster planning, and issues related to data management are some of the main challenges identified in this study. Participatory mapping, Co-Design Projects, hackathons, and crowdfunding are some of the observed citizen science concepts which can be used to address the challenges and strengthen the Disaster Management activities in Sri Lanka. Further studies including interviews and questionnaire surveys were recommended to justify the findings.
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Gabriel, A., & Torres, F. S. (2023). Navigating Towards Safe and Secure Offshore Wind Farms: An Indicator Based Approach in the German North and Baltic Sea. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 609–619). Omaha, USA: University of Nebraska at Omaha.
Abstract: Offshore wind farms (OWFs) have become an increasingly relevant form of renewable energy in recent years, with the German North Sea being one of the most active regions in the world. However, the safety and security of OWF have become increasingly important due to the potential threats and risks associated with their growing share in the security of energy supply. This paper aims to present a comprehensive and systematic indicator-based approach to assess the safety and security status of OWFs in the German North Sea. The approach is based on the results of a survey of people working in the offshore industry and draws on the work published by Gabriel et al. (2022). The results of the study suggest that the indicator-based approach is a useful tool for end users to assess the security status of offshore wind farms and can be used for further research and development.
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Gabriela C Barrera, & Maria C Yang. (2019). Evaluation of Digital Volunteers using a Design Approach: Motivations and Contributions in Disaster Response. 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: With the growth of social media and crowdsourcing in disaster response, further research is needed on the motivations
and contributions of digital volunteers. This study applies a user-centered design approach to understanding how we
might make better tools to support digital volunteers. This user-centered design approach involves stated preference
elicitation methods through an online survey to understand what digital volunteers want in such tools. Through
choice-based conjoint analysis, we contribute to mixed-methods research to gain additional insight into motivations
and user preferences for a set of design features that might be incorporated into an online tool specifically for digital
volunteers. Initial results show preferences for measures of success that were not monetary, which aligned with
directly stated motivations for volunteering. Our findings corroborate with previous research in that feedback to
volunteers is very important, as well as being able to measure the impact of their work.
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Gaëtan Caillaut, Cécile Gracianne, Nathalie Abadie, Guillaume Touya, & Samuel Auclair. (2022). Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 654–663). Tarbes, France.
Abstract: During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task.
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Gah-Kai Leung. (2021). Reducing Flood Risks for Young People in the UK Housing Market. 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. 481–487). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is one of the most serious natural hazards faced in the UK. The Environment Agency estimates that in England alone, about 5.2 million properties are at risk of flooding, or roughly one in six (2009: 3). Flooding imposes significant financial, psychological and social burdens on households and these may be especially acute for young people in the property market, such as renters and first-time buyers. This paper examines how housing-related policy can help alleviate the burdens of flooding on young people in the housing market. First, it canvasses the kinds of damage inflicted when flooding affects properties. Second, it discusses the financial burdens imposed by such damage. Third, it enumerates the financial burdens and benefits of measures to protect against flooding. Fourth, it considers the non-monetary burdens of flooding, in the form of psychological and social burdens. Finally, the paper offers some policy recommendations in light of the preceding discussion.
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Gary Bennett, Lili Yang, & Boyka Simeonova. (2017). A Heuristic Approach to Flood Evacuation Planning. 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. 380–388). Albi, France: Iscram.
Abstract: Flood evacuation planning models are an important tool used in preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. We present a heuristically driven flood evacuation planning model; the proposed heuristic is deterministic, which allows the model to avoid this problem. The determinism of the model means that the optimality of solutions found can be readily verified.
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Gavin Treadgold, James Gunn, Paul Morton, & Simon Chambers. (2018). Developing a regional approach and strategy for geographical information systems for emergency management. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 190–199). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: This paper outlines practitioner work-in-progress in Canterbury, New Zealand, to develop a regional approach for geographical information systems (GIS) for emergency management. This is based upon recent events in Canterbury including earthquakes, floods, and fire; as well as New Zealand-wide work that is being done under the NZ GIS4EM banner. It introduces our approach, discusses a mind map that is being used to track desired data sets, plans to develop applications to support response functions in emergency operations centres, and the goal of using the common data sets as the basis of a common operating picture for Canterbury. Risks and issues associated with this work are highlighted, and then the draft strategy is introduced with desired outcomes and principles to achieve this goal. While initial work is primarily focused on GIS, the expectation is that the approach will be expanded to take a broader information management perspective in future.
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Gerasimos Antzoulatos, Panagiotis Giannakeris, Ilias Koulalis, Anastasios Karakostas, Stefanos Vrochidis, & Ioannis Kompatsiaris. (2020). A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 75–89). Blacksburg, VA (USA): Virginia Tech.
Abstract: Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.
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Gerhard Rauchecker, & Guido Schryen. (2018). Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 69–82). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system, which we develop in joint cooperation with a disaster management authority and a software development company.
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Giulio Palomba, Alessandro Farasin, & Claudio Rossi. (2020). Sentinel-1 Flood Delineation with Supervised Machine Learning. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1072–1083). Blacksburg, VA (USA): Virginia Tech.
Abstract: Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.
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Gkika, I., Pattas, D., Konstantoudakis, K., & Zarpalas, D. (2023). Object detection and augmented reality annotations for increased situational awareness in light smoke conditions. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 231–241). Omaha, USA: University of Nebraska at Omaha.
Abstract: Innovative technologies powered by Computer Vision algorithms can aid first responders, increasing their situ ational awareness. However, adverse conditions, such as smoke, can reduce the efficacy of such algorithms by degrading the input images. This paper presents a pipeline of image de-smoking, object detection, and augmented reality display that aims to enhance situational awareness in smoky conditions. A novel smoke-reducing deep learning algorithm is applied as a preprocessing step, before state-of-the-art object detection. The detected objects and persons are highlighted in the user’s augmented reality display. The proposed method is shown to increase detection accuracy and confidence. Testing in realistic environments provides an initial evaluation of the method, both in terms of image processing and of usefulness to first responders.
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Gonzalez, J. J., & Eden, C. (2023). Devising Mitigation Strategies With Stakeholders Against Systemic Risks in a Pandemic. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1000–1013). Omaha, USA: University of Nebraska at Omaha.
Abstract: Understanding and managing systemic risk has huge importance for disaster risk reduction in our globally connected world. The COVID-19 pandemic is a prominent case for the global impact of systemic risk. Did so the added urgency of the pandemic systemic risk trigger such paradigm shift? The use of qualitative modelling of systemic risk has progressed the field, particularly when policy makers need support urgently and want to utilize a range of interdisciplinary expertise. We have extended to disaster risk reduction a method for causal mapping for problem solving and strategy development targeting complex project management. Our approach delivers useful, useable, and used mitigation to systemic risk in a pandemic using participatory modelling with practitioners, domain experts and power-brokers.
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Grace, R., Montarnal, A., Petitdemange, E., Rutter, J., Rodriguez, G. R., & Potts, M. (2023). Collaborative Information Seeking during a 911 Call Surge: A Case Study. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 649–662). Omaha, USA: University of Nebraska at Omaha.
Abstract: This case study examines collaborative information seeking in a public-safety answering point during a 911 call surge that occurred when a man fired an assault rifle at police officers and evaded capture for nearly an hour in March 2020. Overwhelmed by questionable and imprecise reports from 911 callers, telecommunicators and on scene responders began working together to conduct broad and deep searches for the shooter. Whereas broad searches improved the scope of information gathering by identifying multiple, albeit questionable and imprecise, reports of the suspect’s location, deep searches improved the quality of information gathering by investigating 911 callers’ reports using drone, helicopter, and patrol units. These findings suggest requirements for collaborative information seeking in public-safety answering points, including capabilities to conduct broad and deep searches using next-generation 911 technologies, and command and control requirements for triaging these search tasks within inter-organizational emergency response systems.
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Grégoire Burel, & Harith Alani. (2018). Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 597–608). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings.
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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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Guillaume Lambert, Bruno Fontaine, Michel Monneret, & Mourad Madani. (2019). How to build an innovative C2 system supporting individual-centric emergency needs ? 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: The paper describes the need for, and work in progress to provide the French population with
a modern emergency communication infrastructure that uses open source components to
deliver real time communications from smart phones as well as traditional routes.
The article puts forward the vision of the NexSIS 18-112 project aimed at designing and
implementing the next generation AI enhanced emergency services response platform for
France. The vision and ambition of the NexSIS 18-112 system is to rewrite the command and
control system from scratch at a national level, providing it with state of the art functionalities.
Anticipating the future deployment of 5G networks, the work described in the article explains
how to ensure the transition of the legacy emergency operation systems to an operational IPbased
model, capable of offering voice, video, Instant Messaging, and Real Time Text (RTT)
services to emergency services? operators.
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Guillermo Romera Rodriguez. (2023). Parler, Capitol Riots, Alt-Right and Radicalization in Social Media. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 268–277). Palmerston North, New Zealand: Massey Unversity.
Abstract: Social media platforms have risen in popularity since their inception. These platforms have since then come to be at the forefront of controversies, from being accused of election interference to, more recently, disseminating fake news and campaigns to sway political behavior. One such episode took place on January 6 when a group of individuals stormed the United States Capitol, and the social media platform Parler came under scrutiny. The platform was accused of being a place for right-wing extremists and Trump supporters who claimed the 2020 election was fraudulent. Initial reports suggested these individuals used Parler to organize and call others to action. This paper explores the feasibility of using social media to detect alt-right radicalization and examines its possible relation to the Capitol Insurrection and Parler. Moreover, we examine if those events could have been detected and averted through the investigation of the platform.
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Guoqin Ma, & Chittayong Surakitbanharn. (2019). Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables. 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 a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate
to the most devastated areas. To better understand the correlation between social media and physical damage, we
compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information
(as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning
to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to
determine the predictive capability of insurance claims as a proxy for damage. We find that Tweets alone result in a
poorly fitted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind
level) and socio-economic factors (e.g., population density, education, Internet access) improves the model?s fit.
Such models contribute to the knowledge base that may allow social media to predict damage in real-time.
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Hafiz Budi Firmansyah, Jesus Cerquides, & Jose Luis Fernandez-Marquez. (2022). Ensemble Learning for the Classification of Social Media Data in Disaster Response. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 710–718). Tarbes, France.
Abstract: Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.
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