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Christian Iasio, Ingrid Canovas, Elie Chevillot-Miot, & Tendry Randramialala. (2022). A new approach to structured processing of feedback for discovering and investigating interconnections, cascading events and disaster chains. In N.Matta, H. Purohit, H. Karray, A. DI Nicola, & L. Elmhadi (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1–99). Tarbes, France: Enit.
Abstract: Post-disaster information processing is relevant for the continuous improvement of operations and the reduction of risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.
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Alva Lindhagen, Anton Björnqvist, & Peter Berggren. (2022). A meta-evaluation of Swedish evaluations of COVID-19 pandemic management. In N.Matta, H. Purohit, H. Karray, A. DI Nicola, & L. Elmhadi (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1–99). Tarbes, France: Enit.
Abstract: The COVID-19 pandemic has had a global impact om society. Different countries and organizations have chosen different approaches to manage this crisis. This paper aims to describes how public Swedish actors (county administrative boards, municipalities, and regional councils) evaluated their management of the COVID-19 pandemic. Applying a meta-evaluation approach to open reports from the public organizations means collecting evaluation reports, determining if they fit the inclusion criteria, and if so, include them in the analysis. Included reports were categorized and given points indicating different types of merits. In addition, a thematic analysis of conclusions was conducted. 110 evaluation reports from 98 different organizations are included in the analysis. The importance of evaluating, having a structure for data collection, analysis, and reporting is reflected in the quality of the reports. Four identified themes offers an understanding of areas in need for development among Swedish regional councils, municipalities, and county administrative boards.
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Dario Salza, Edoardo Arnaudo, Giacomo Blanco, & Claudio Rossi. (2022). A 'glocal' approach for real-time emergency event detection in Twitter. In N.Matta, H. Purohit, H. Karray, A. DI Nicola, & L. Elmhadi (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1–99). Tarbes, France: Enit.
Abstract: Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a 'glocal' approach, i.e., offering a global coverage while detecting events at local (municipality level) scale.
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Duygu Pamukcu, Christopher Zobel, & Yue Ge. (2022). A DEA-based Approach for Managing Performance of Public Service Systems During a Disaster. In N.Matta, H. Purohit, H. Karray, A. DI Nicola, & L. Elmhadi (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1–99). Tarbes, France: Enit.
Abstract: In addition to their normal task of supporting community participation, engagement, and improved information access, information technology-based public service systems are also essential for maintaining critical services and providing effective communication with citizens before, during, and after emergencies. This study focuses on the impacts of disaster events on the operational performance of such service systems and it discusses opportunities for managing service efficiency by rearranging and reallocating resources during emergencies. To the best of our knowledge, this is the first attempt to provide a practical method for improving the relative efficiency of such public service systems in such a context. We suggest a Data Envelopment Analysis (DEA) approach for quantifying the relative efficiencies associated with service requests from an input-output-based standpoint, and discuss the Orange County (Florida) 311 system, in the context of the COVID-19 pandemic, as an example of how such operational efficiency can be managed during a disruption.
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Pooneh Mousavi, & Cody Buntain. (2022). “Please Donate for the Affected”: Supporting Emergency Managers in Finding Volunteers and Donations in Twitter Across Disasters. In N.Matta, H. Purohit, H. Karray, A. DI Nicola, & L. Elmhadi (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1–99). Tarbes, France: Enit.
Abstract: Despite the outpouring of social support posted to social media channels in the aftermath of disaster, finding and managing content that can translate into community relief, donations, volunteering, or other recovery support is difficult due to the lack of sufficient annotated data around volunteerism. This paper outlines three experiments to alleviate these difficulties. First, we estimate to what degree volunteerism content from one crisis is transferable to another by evaluating the consistency of language in volunteer- and donation-related social media content across 78 disasters. Second it introduces methods for providing computational support in this emergency support function and developing semi-automated models for classifying volunteer- and donation-related social media content in new disaster events. Results show volunteer- and donation-related social media content is sufficiently similar across disasters and disaster-types to warrant transferring models across disasters, and we evaluate simple resampling techniques for tuning these models. We then introduce and evaluate a weak-supervision approach to integrate domain knowledge from emergency response officers with machine learning models to improve classification accuracy and accelerate this emergency support in new events. This method helps to overcome the scarcity in data that we observe related to volunteer- and donation-related social media content.
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