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Author Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel (eds)
Title (up) 18th ISCRAM Conference Proceedings Type Conference Volume
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages
Keywords
Abstract The theme of ISCRAM 2021 is ?Embracing the Interdisciplinary Nature of Crisis Management.? These

proceedings highlight the range of interdisciplinary research required to understand the design, behavior,

and performance of crisis and emergency management systems. We are pleased to present the included

papers, which offer excellent contributions on a wide range of topics related to the use of information

systems in crisis response and management.
Address
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-1-949373-61-5 Medium
Track Proceedings Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2396
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Author Therese Habig; Richard Lüke; Simon Gehlhar; Torben Sauerland; Daniel Tappe
Title (up) A Consolidated Understanding of Disaster Community Technologies Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 778-791
Keywords Disaster Community Technologies, social media and crowdsourcing, categorization and classification schema, knowledge base
Abstract Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview.
Address safety innovation center; safety innovation center; safety innovation center; safety innovation center; safety innovation center
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes habig@safetyinnovation.center Approved no
Call Number ISCRAM @ idladmin @ Serial 2373
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Author Lucas Dorigueto; Carlos Brumatti; Erick Figueiredo; Jugurta Lisboa-Filho
Title (up) A Framework for Landslide Information Management Systems Development Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 515-526
Keywords Disaster Information Management Systems, Landslide, Interoperability, Volunteered Geographic Information
Abstract Volunteered Geographic Information (VGI) integrated with Disaster Information Management Systems (DIMS) has great potential to assist managers and the community in times of emergency. However, there is little research focusing on integrating VGI with DIMS, in addition, there are a lack of use of standards of interoperability and emergency, which can impair interoperability and the quality of the information contained in these systems. This work presents a fully interoperable framework aimed at the construction of DIMS, which integrates official data and VGI through ISO and OGC standards, allowing managers and the community to work with official data and VGI in order to assist managers in decision making. To show the viability of the framework, a case study using data from the risk situation of dams located in the municipality of Barão de Cocais in Brazil was carried out.
Address Universidade Federal de Viçosa (UFV); Universidade Federal de Viçosa (UFV); Universidade Federal de Viçosa (UFV); Universidade Federal de Viçosa (UFV)
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes lucas.dorigueto@ufv.br Approved no
Call Number ISCRAM @ idladmin @ Serial 2352
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Author Sindisiwe Magutshwa; Jaziar Radianti
Title (up) A Qualitative Risk Identification Framework for Cyber-Physical-Social Systems Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 377-390
Keywords Cyber physical systems, Cyber physical Social Systems, risk, vulnerability, mission critical
Abstract As information and communication technologies, real-world physical systems, and people become interconnected in critical infrastructure, attention has shifted to the operations of Cyber-Physical-Social Systems (CPSS). CPSS are progressively integrated in core critical infrastructure organisational processes to achieve a combination of benefits. However, the high degree of integration of technology into human society and mission-critical processes leads to an increase in complexity and introduces novel risks and vulnerabilities. These novel constraints extend beyond what is known from previous cyber-physical and critical infrastructure systems studies and prompt the need for revised risk perception and identification methodologies. This paper aims to develop a novel qualitative risk identification framework that is used in the identification of risk and vulnerability in CPSS ecosystems deployed in critical infrastructure or mission-critical organisational processes. The framework emphasizes interactions between humans and the system making it possible to identify and under-stand how non-technical risk impacts the CPSS ecosystem.
Address University of Agder; University of Agder
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Enhancing Protection of Critical Infrastructures Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes sindisiwe.magutshwa@uia.no Approved no
Call Number ISCRAM @ idladmin @ Serial 2340
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Author Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang
Title (up) A Question Answering System Applied to Disasters Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 2-16
Keywords Emergency Management, Disaster, Natural Language Processing, Deep Learning
Abstract In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph.
Address Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes gxj19@mails.tsinghua.edu.cn Approved no
Call Number ISCRAM @ idladmin @ Serial 2308
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Author Sofia Kostakonti; Ramona Velea; Vassilis Papataxiarhis; Daniele Del Bianco; Uberto Delprato; Stathes Hadjiefthymiades
Title (up) A semantic approach for modeling vulnerability of communities Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 305-318
Keywords Community vulnerability, semantic modeling, community resilience, knowledge representation and reasoning
Abstract In this paper, we propose the use of semantic technologies for the representation of concepts and relationships required for the modeling of vulnerability data for local communities. First, we discuss the concepts of vulnerability and resilience and we try to identify the relationship between the two. We provide some background knowledge and we present basic characteristics of the two concepts. Next, we discuss the motivation behind the use of semantic technologies, and we show how the proposed framework can address existing challenges in terms of vulnerability assessment. The core part of this paper focuses on the semantic representation of community vulnerability aspects. We give an overview of the layered semantic framework consisting of interconnected ontological models and we provide a set of use-cases where the use of semantic-based modeling and query answering can prove beneficial in terms of assessing vulnerability.
Address National and Kapodistrian University of Athens; Institute of International Sociology of Gorizia; National and Kapodistrian University of Athens; Institute of International Sociology of Gorizia; Intelligence for Environment and Security; National and Kapod
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Data and Resilience: Opportunities and Challenges Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes sofkost@di.uoa.gr Approved no
Call Number ISCRAM @ idladmin @ Serial 2335
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Author Ivar Svare Holand; Peter Mozelius; Trond Olav Skevik
Title (up) A structured and dynamic model for emergency management exercises Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 186-197
Keywords Emergency exercises, Vulnerability assessment, Non-linear emergency exercise model, Norwegian-Swedish cross-border collaboration, Gaining Security Symbiosis (GSS) projects
Abstract Emergencies are management challenges, and emergency exercises that involve multiple collaborating parties is a means towards mastering them. Such exercises are often conducted in a virtual training environment based on complex disaster scenarios. The reported study was carried out using a requirement-focused design approach. The aim was to describe and discuss a relevant design for lean, dynamic, and cost-efficient emergency management exercise systems. Data were gathered from a literature study and analyses of earlier emergency management projects in which the authors had participated. Despite the complexity of many current emergency management exercises, the scenarios usually involve only the response phases and have a linear structure that hinders both didactic aspects and the software structure. The conclusion drawn from the study is that an emergency management exercise model should focus on managing the activities that correspond to alternatives that unfold from a dynamic scenario. Finally, the authors recommend the principles of alternate reality games as a way towards more dynamic and cost-efficient emergency exercise systems.
Address Nord University; Mid Sweden University; Nord University
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Analytical Modeling and Simulation Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes ivar.s.holand@nord.no Approved no
Call Number ISCRAM @ idladmin @ Serial 2324
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Author Maki Tagashira; Toshihiro Osaragi
Title (up) Accessibility Assessment of Vulnerable Roadside Areas after a Major Earthquake Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 553-566
Keywords Large earthquake, accessibility assessment, emergency activity, building collapse, road blockage
Abstract In order to reduce human casualty after a large earthquake, it is vital to secure the traffic function of main roads. Local governments promote the seismic reinforcement of roadside buildings, however, the project is not going well as planned. There is a high demand for appropriate information of its effect. In this paper, we proposed a method to identify the roadside areas with vulnerable accessibility to disaster bases after a large earthquake. First, we defined the accessibility indices; Link Isolation ratio (LI ratio) and Network Isolation ratio (NI ratio). Then, using the simulation model, we evaluated the accessibility to disaster base hospitals using emergency transportation roads in the Tokyo Metropolitan Area. LI ratio tended to be low in areas with a sparse road network. Furthermore, some hospitals indicated a severely high NI ratio. In secondary medical areas with these hospitals, it is necessary to consider the measures to improve accessibility.
Address Tokyo Institute of Technology; Tokyo Institute of Technology
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Planning, Foresight and Risk Analysis Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes tagashira@arch.titech.ac.jp Approved no
Call Number ISCRAM @ idladmin @ Serial 2355
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Author Julien Coche; Jess Kropczynski; Aurélie Montarnal; Andrea Tapia; Frédérick Bénaben
Title (up) Actionability in a Situation Awareness world: Implications for social media processing system design Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 994-1001
Keywords Actionable Information, Situation Awareness, Social Media, Crisis Management
Abstract The field of crisis informatics now has a decade-long history of designing tools that leverage social media to support decision-makers situation awareness. Despite this history, there remains few examples of these tools adopted by practitioners. Recent fieldwork with public safety answering points and first responders has led to an awareness of the need for tools that gather actionable information, rather than situational awareness alone. This paper contributes to an ongoing discussion about these concepts by proposing a model that embeds the concept of actionable information into Endsley's model of situation awareness. We also extend the insights of this model to the design implications of future information processing systems.
Address IMT Mines Albi; University of Cincinnati; Ecole des Mines d'Albi Carmaux; The Pennsylvania State University; Ecole des Mines d'Albi-Carmaux
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Visions for Future Crisis Management Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes coche.emac@gmail.com Approved no
Call Number ISCRAM @ idladmin @ Serial 2391
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Author Tina Mioch; Reinier Sterkenburg; Tatjana Beuker; Mark A. Neerincx
Title (up) Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 62-70
Keywords Situation Awareness, Actionability, Decision support, Chemical hazard
Abstract Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability.
Address Tno; Tno; Tno; Tno
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes tina.mioch@tno.nl Approved no
Call Number ISCRAM @ idladmin @ Serial 2313
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Author Ke Wang; Yongsheng Yang; Genserik Reniers; Jian Li; Quanyi Huang
Title (up) An Attribute-based Model to Retrieve Storm Surge Disaster Cases Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 567-580
Keywords Storm surge disaster, multiple attributes, retrieval model, affected region prediction
Abstract In China, storm surge disasters cause severe damages in coastal regions. One of the most important tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides useful information for the government to make real-time response plans.
Address Tsinghua University; Tsinghua University; KU Leuven; Tsinghua University; Tsinghua University
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Planning, Foresight and Risk Analysis Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes wangke16@mails.tsinghua.edu.cn Approved no
Call Number ISCRAM @ idladmin @ Serial 2356
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Author Enrique Caballero; Angel Madridano; Dimitrios Sainidis; Konstantinos Konstantoudakis; Petros Daras; Pablo Flores
Title (up) An automated UAV-assisted 2D mapping system for First Responders Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 890-902
Keywords UAV, drone, 2D Mapping, Swarm, First Responders, Emergency Operations
Abstract Recent advances in the Unmanned Aerial Vehicles (UAVs) sector have allowed such systems to carry a range of sensors, thus increasing their versatility and adaptability to a wider range of tasks and services. Furthermore, the agility of these vehicles allows them to adapt to rapidly changing environments making them an effective tool for emergency situations. A single UAV, or a swarm working in collaboration, can be a handy and helpful tool for First Responders (FRs) during mission planning, mission monitoring, and the tracking of evolving risks. UAVs, with their on-board sensors, can, among other things, capture visual information of the disaster scene in a safe and quick manner, and generate an up-to-date map of the area. This work presents a system for UAV-assisted mapping optimized for FRs, including the generation of routes for the UAVs to follow, data collection and processing, and map generation.
Address Drone Hopper; Drone Hopper; Centre for Research & Technology, CERTH; Centre for Research & Technology, CERTH; Centre for Research & Technology, CERTH; Drone Hopper
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Technologies for First Responders Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes e.caballero@drone-hopper.com Approved no
Call Number ISCRAM @ idladmin @ Serial 2381
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Author Shivam Sharma; Cody Buntain
Title (up) An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 808-815
Keywords covid19, twitter, trecis, cross-validation, machine learning, transfer learning
Abstract In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data.
Address New Jersey Institute of Technology; New Jersey Institute of Technology
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes cbuntain@njit.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2375
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Author Nathan Elrod; Pranav Mahajan; Monica Katragadda; Shane Halse; Jess Kropczynski
Title (up) An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 345-358
Keywords Social media analytics, situational awareness, sentiment analysis, n-grams, social network analysis
Abstract During the COVID-19 health crisis, local public offcials expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health offcials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health offcials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results oer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.
Address University of Cincinnati; University of Cincinnati; University of Cincinnati; University of Cincinnati; University of Cincinnati
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Disaster Public Health & Healthcare Informatics in the Pandemic Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes elrodnj@ucmail.uc.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2338
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Author Duygu Pamukcu; Christopher Zobel; Yue Ge
Title (up) Analysis of Orange County, Florida 311 System Service Requests During the COVID-19 Pandemic Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 208-217
Keywords Disaster management, COVID-19, 311 system, Orlando
Abstract The Orlando metropolitan area in Florida, where Walt Disney World is located, is intimately familiar with impacts of natural disasters because of the yearly threat of hurricanes in the southeastern United States. One of the tools that has aided them in their efforts to monitor and manage such disasters is their 311 non-emergency call system, through which local residents can issue requests to the municipality for disaster-related information or other services. This paper provides a preliminary examination of the potential for the Orange County 311 system to provide actionable information to them in support of their efforts to manage a different type of disaster: the COVID-19 pandemic. The potential of the system to support the County in this context is illustrated through several preliminary analyses of the complete set of service requests that were registered in the first ten months of 2020.
Address Virginia Tech; Virginia Tech; University of Central Florida
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Analytical Modeling and Simulation Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes duygu@vt.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2326
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Author Nada Matta; Thomas Godard; Guillaume Delatour; Ludovic Blay; Franck Pouzet; Audrey Senator
Title (up) Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 17-27
Keywords Social Media analysis, TextMining, sentiment analysis, crisis management, decision making
Abstract Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data.
Address University of Technology of Troyes; University of Technology of Troyes; University of Technology of Troyes; VISOV; CS Group; ENSOSP
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes nada.matta@utt.fr Approved no
Call Number ISCRAM @ idladmin @ Serial 2309
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Author Zeno Franco; Chris Davis; Adina Kalet; Michelle Horng; Johnathan Horng; Christian Hernandez; Karen Dotson; Andrew Yaspan; Ajay Kumar; Bas Lijnse
Title (up) Augmenting Google Sheets to Improvise Community COVID-19 Mask Distribution Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 359-375
Keywords Logistics, face masks, Google Sheets, modular software, community engagement
Abstract Face mask scarcity in the United States hindered early infection control efforts during the COVID-19 pandemic. Areas with a history of racial segregation and poverty experienced differential COVID-19 death and morbidity rates. Supplying masks equitably and rapidly became an urgent public health priority. A partnership between a local manufacturer with available polypropylene fabric and the Medical College of Wisconsin, which had the capability to assemble and distribute masks, was formed in April, 2020. An improvised logistics framework allowed for rapid distribution more than 250,000 masks, and later facilitated hand-off to other organizations to distribute over 3 million masks. Using an action research framework three phases of the effort are considered, 1) initial deliveries to community clinics, 2) equitable distribution to community agencies while under “safer at home” orders, and 3) depot deliveries and transfer of logistics management as larger agencies recovered. A multi-actor view was used to interrogate the information needs of faculty and staff remotely directing distribution, medical student volunteers delivering masks, and the manufacturer monitorng overall inventory. Logistics information was managed using Google Sheets augmented with a small SQLite component. A phenomenological view, toggling back and forth from the “socio” to the “technical” provides detailed insight into the strengths and limitations of digital solutions for humanitarian logistics, highlighting where paper-based processes remain more efficient. This case study suggests that rather than building bespoke logistics software, supporting relief efforts with non-traditional responders may benefit from extensible components that augment widely used digital tools.
Address Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin; Marquette University; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin; Code for Milwaukee; University of Muenster; Netherlan
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Disaster Public Health & Healthcare Informatics in the Pandemic Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes zfranco@mcw.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2339
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Author Zijun Long; Richard Mccreadie
Title (up) Automated Crisis Content Categorization for COVID-19 Tweet Streams Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 667-678
Keywords COVID-19, Tweet Classification, Crisis Management, Deep Learning
Abstract Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.
Address University of Glasgow; University of Glasgow
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes 2452593L@student.gla.ac.uk Approved no
Call Number ISCRAM @ idladmin @ Serial 2363
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Author Yitong Li; Duoduo Liao; Jundong Li; Wenying Ji
Title (up) Automated Generation of Disaster Response Networks through Information Extraction Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 431-438
Keywords Disaster response, Stakeholder collaboration, Natural language processing, Network generation
Abstract Following a disaster, maintaining and restoring community lifelines require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating stakeholder collaboration networks (SCNs), this research proposes a systematic approach for reliably extracting stakeholder collaboration information from texts and automatically generating SCNs. In the proposed approach, stakeholders and their interactions are automatically extracted from texts through a natural language processing technique--Named Entity Recognition. Once extracted, the collaboration information is stored into structured datasets to automate the generation of SCNs. A case study on stakeholder collaboration in response to Hurricane Harvey is used to demonstrate the feasibility and applicability of the proposed approach. Overall, the proposed approach achieves the reliable and automated generation of SCNs from texts, which largely reduces practitioners' interpretation loads and eases the data collection process.
Address George Mason University; George Mason University; University of Virginia; George Mason University
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Enhancing Resilient Response in Inter-organizational Contexts Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes wji2@gmu.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2344
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Author Yohann Chasseray; Anne-Marie Barthe-Delanoë; Stéphane Négny; Jean-Marc Le Lann
Title (up) Automated unsupervised ontology population system applied to crisis management domain Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 968-981
Keywords Automated knowledge extraction, Crisis management systems, Ontologies, Experience feedback exploitation, Background knowledge acquisition
Abstract As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster.
Address Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Centre Génie Industriel, Université deToulouse, IMT Mines Albi, France; Laboratoire de Génie Chimique, Universitéde Toulouse, CNRS, INPT, UPS, Toulouse,France; Laborat
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Visions for Future Crisis Management Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes yohann.chasseray@inp-toulouse.fr Approved no
Call Number ISCRAM @ idladmin @ Serial 2389
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Author Boris Petrenj; Paolo Trucco
Title (up) Blockchain-based Solutions to support inter-organisational Critical Infrastructure Resilience Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 982-993
Keywords Critical Infrastructure, Blockchain, Research agenda, Resilience, Capability, Inter-organizational
Abstract This conceptual paper critically discusses opportunities for and challenges to the development and exploitation of blockchain-based solutions for resilience management at inter-organizational level of interdependent Critical Infrastructure (CI) systems. The main premise behind this idea is that trustful information-sharing and inter-institutional collaboration are the key elements of government and private sector efforts to build CI resilience (CIR). The discussion presents a vision that the adoption and adaptation of Blockchain Technology (BCT) could significantly improve the way a network of stakeholders prepares for and performs in face of inevitable CI disruptions. Even though BCT is regarded as technological innovation, the impacts go far beyond information systems. BCT application in this domain would entail significant benefits to organizational, managerial, legal and social issues, but would require adequate operational and organizational changes. We discuss how interdisciplinary approach (BCT and CIR) could address existing challenges, how it could introduce new challenges and how it could support other approaches and paradigms currently being regarded as the future of risk and resilience management. Even though the discussion in this paper is focused on Critical Infrastructure resilience, each point also applies to Crisis/Disaster management domain in general. This is a preliminary overview with the aim to stimulate further discussions and point to possible new, disruptive and interdisciplinary research avenues. To this end, a possible research agenda is eventually proposed.
Address Politecnico di Milano; Politecnico di Milano
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Visions for Future Crisis Management Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes boris.petrenj@polimi.it Approved no
Call Number ISCRAM @ idladmin @ Serial 2390
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Author Anmol Haque; Duygu Pamukcu; Ruixiang Xie; Mohsen Zaker Esteghamati; Margaret Cowell; Jennifer L. Irish
Title (up) Cascading Effects of Mass Gatherings on COVID-19 Infections from a Multi-hazard Perspective: A Case Study of New York City Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 218-227
Keywords COVID-19 pandemic, Mass gatherings, Multi-hazard, Vulnerability
Abstract The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed.
Address Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech; Virginia Tech
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Analytical Modeling and Simulation Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes anmol91@vt.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2327
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Author Stella Polikarpus; Tobias Ley; Katrin Poom-Valickis
Title (up) Collaborative Authoring of Virtual Simulation Scenarios for Assessing Situational Awareness Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 229-237
Keywords situational awareness (SA), virtual simulation, virtual simulation scenario, process model, Effective Command Behavioral Marker Framework
Abstract Situational awareness (SA), the ability to perceive, comprehend and predict situation around you and it is a key in attending any incident as critical foundation for successful decision-making. Because incidents are solitary events, development and assessment of SA presents a significant challenge. In this article we analyze the authoring process of twenty-two scenarios implemented in the XVR on-scene virtual simulation software used to assess rescue incident commanders' (ICs) SA. To allow the scenarios to be used by different assessors, the Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM) was developed. In Estonia, 473 assessments were recorded in Effective Command database and analysed by all three levels of SA as recommended by Endsley (2000). Introduction of CAPM resulted in scenarios being re-used by different assessors for authentic SA measuring. In the last sections of this article, we introduce our suggestions to improve virtual scenario design and SA research.
Address Tallinn University; Tallinn University; Tallinn University
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Command & Control Studies Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes stella.polikarpus@gmail.com Approved no
Call Number ISCRAM @ idladmin @ Serial 2328
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Author Aikaterini Christodoulou; John Lioumbas; Kostantinos Zambetoglou; Nikoletta Xanthopoulou
Title (up) Combined innovative technologies for ensuring water safety in utilities: The city of Thessaloniki case study Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 929-934
Keywords Water safety, satellite images, drones, risk assessment
Abstract Innovative technologies such as monitoring the quality of surface water aquifers with satellite images, applying UAV (Unmanned Aerial Vehicle) and drone technology for a variety of operations, water quality measurements with improved techniques along with IoT (Internet of Things) and ICT (Information and Communication Technologies), can provide sufficient data for enhancing water safety in urban water utilities. Specifically, these data could be an effective tool for improving risk assessment process and management of water supply systems. Nevertheless, till now, there is a relative lack of published works that validate the efficiency of combing these technologies on water safety processes by incorporating most of them with a systematic way and during real working conditions in water utilities. This work aims to present the preliminary design concept of a platform that embraces innovating water safety technologies planned to be applied to Thessaloniki's Water Supply and Sewerage Co. S.A Standard Operating Procedures (SOP).
Address Thessaloniki Water Supply and Sewerage Co. S.A (EYATH SA); Thessaloniki Water Supply and Sewerage Co. S.A (EYATH SA); Thessaloniki Water Supply and Sewerage Co. S.A (EYATH SA); Thessaloniki Water Supply and Sewerage Co. S.A (EYATH SA)
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Technologies for First Responders Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes catchristo@eyath.gr Approved no
Call Number ISCRAM @ idladmin @ Serial 2385
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Author Hongmin Li; Doina Caragea; Cornelia Caragea
Title (up) Combining Self-training with Deep Learning for Disaster Tweet Classification Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 719-730
Keywords Domain Adaptation, Self-training, Crisis Tweets Classification, BERT, CNN
Abstract Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets.
Address Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University; Department of Computer Science, University of Illinois at Chicago
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track Social Media for Disaster Response and Resilience Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes hongminli@ksu.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2367
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