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Pengfei Zhou, Tao Chen, Guofeng Su, Bingxu Hou, & Lida Huang. (2020). Research on the Forecasting and Risk Analysis Method of Snowmelt Flood. 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. 545–557). Blacksburg, VA (USA): Virginia Tech.
Abstract: Risk analysis of snowmelt flood is an urgent demand in cold highland areas. This paper focuses on the method for the rapid and reliable forecast of daily snowmelt, snow water runoff, and snowmelt flood risk. A neural network algorithm is used to calculate snow density distribution, snow depth and snow-water equivalent with the brightness temperature data. Then, daily snowmelt is predicted using the degree-day factor method with the temperature distribution. On this basis, we use the steepest descent method and Manning formula with hydrographic information to simulate snow water runoff. We also propose a method to predict the snowmelt flood risk with the geographic feature and historical flood data. The evaluated risk is compared with monitored data in the Xinjiang Autonomous Region of China, which shows good consistency. At last, we develop a risk analysis system to generate the snowmelt flood risk map and provide risk analysis service.
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Patricia Quiroz-Palma, Ma Carmen Penadés, & Ana-Gabriela Núñez. (2020). Resilience Learning for Emergency Plan Management in Organizations. 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. 558–567). Blacksburg, VA (USA): Virginia Tech.
Abstract: Many governments, organizations, practitioners and researchers involved in collaboration on resilience in emergency management are agreed that this is a key aspect. The QuEP+R framework aims to improve resilience in an organization's emergency plan management, in which the stakeholders must be adequately prepared and trained for their responsibilities in the emergency plan, providing techniques that propose the improvement of the emergency plan besides resilience. However, for these techniques to be effective, organizations need the theoretical resilience proposed in QuE+R to be implemented. The CiET framework was designed for this purpose and has learning objectives and training contents related to QuEP+R techniques to train stakeholders. The CiET capability plan contents have been classified by resilience dimensions towards the optimization of resilience in emergency plan management. The integration is supported by I+R-Tool, which generates the capability plans automatically from the results of the QuEP+R assessment, which outcomes in a stakeholder's effective training, contributing to the optimization and improvement of the resilience, therefore, in improving the quality of emergency plans. Hence, the aim remains to search for the continuous improvement of the emergency plan management within organizations.
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Lars Gerhold, Roman Peperhove, & Edda Brandes. (2020). Using Scenarios in a Living Lab for improving Emergency Preparedness. 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. 568–579). Blacksburg, VA (USA): Virginia Tech.
Abstract: Emergency preparedness and management processes are highly influenced by the use of digital technologies. Unfortunately, due to their rapid development, stakeholders from civil protection as well as policy makers often are not aware of new technological possibilities, their potentials and risks. This paper offers a methodological approach to experience evolving technologies by using scenarios in a living lab, equipped with demonstrators from recent research projects. The scenarios are presented to stakeholders from civil protection and policy making by telling a future story about the potential usage of emerging technologies. The Future Security Lab allows addressees to see, understand and use technologies that may become relevant within the next five to ten years and so a profound basis for knowledge transfer is offered. The case study “Digitalization of Emergency Preparedness 2025” demonstrates how scenarios can be used to integrate demonstrators in stories about the future of civil protection. First results of an evaluation provide positive feedback from attendees.
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Alexander Gabriel, Florian Klein, & Frank Fiedrich. (2020). Modelling of Passenger Handling Processes in Railway Stations – A Mixed-Methods Approach. 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. 580–592). Blacksburg, VA (USA): Virginia Tech.
Abstract: The constantly increasing number of passengers using public transportation leads to an expansion of the ser-vices offered by public transportation companies. The existing transportation infrastructures, especially rail-way stations, can only partly cope with this rapid growth. There is already overcrowding on platforms and access routes, especially during disruptions caused by natural disasters or major public events. This crowding may result in personal injury or shutdown of operations for safety reasons. The research project CroMa aims at improving robustness, safety, security and performance of railway stations at peak loads. The paper contributes thereto by developing an approach to assess railway infrastructure in terms of the risk of overcrowding. The core of this research is to combine qualitative workshop results with quantitative database analysis. Furthermore, the paper gives an outlook on the ongoing process model development as a basis for a semi-quantitative evaluation tool for railway stations applicable by end users.
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Ana-Gabriela Núñez, Sebastián Cedillo, Andrés Alvarado Martínez, & Ma Carmen Penadés. (2020). Towards the Building of a Resilient City able to Face Flood Risk Scenarios. 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. 593–601). Blacksburg, VA (USA): Virginia Tech.
Abstract: Despite the efforts that have been made to inform the community about the possible environmental risks, there is still a general lack of information. Currently, we are working on a flood risk scenario focused on a proposal towards a resilient culture together with the support of Information Technologies (IT) as a way to manage information. The goal is twofold: (i) on the one hand, to manage data in a small scenario to analyze and process the data collected from sensors in different sites in a micro-basin. Data get from data processing such as flow and velocity will then be the input data for hydraulic models to predict floods downstream; (ii) on the other hand, to publicize the predictions and the data already processed means people can benefit from information on flood risks, and the different participants may change their perception and consider cooperating in improving resilience.
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Miguel Ramirez de la Huerga, Victor A. Bañuls, Pilar Ortiz Calderon, & Rocio Ortiz Calderon. (2020). A Delphi-Based Approach for Analysing the Resilience Level of Local Goverments in a Regional Context. 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. 602–611). Blacksburg, VA (USA): Virginia Tech.
Abstract: This article shows the research process carried out by Regional Government of southern Europe, with more than 8 million citizens, to create an Information System to serve as a diagnostic and certification model for the resilience level of the municipalities of that region. This Information System will allow the local authorities of the regional governments to know in what situation they are and what they should do to improve their resilience level. The research framework is based on the best practices in urban resilience. One of the relevant characteristics of the work is the integration of the knowledge of a very heterogeneous group of experts for the identification of the special needs of the target region that has been articulated through a Delphi process.
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Manon Grest, Matthieu Lauras, & Benoit Montreuil. (2020). A Humanitarian Supply Chain Maturity Model. 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. 613–621). Blacksburg, VA (USA): Virginia Tech.
Abstract: Over the past decades, humanitarian organizations have largely been criticized for their lack of effectiveness regarding their mission of assisting vulnerable population. However, few researches have investigated what ideal should humanitarian organizations tend toward and the path to undertake in such transformation. In this perspective, this paper intends to overcome this situation by proposing a supply chain maturity model specifically addressed to the humanitarian sector. In the form of a two-dimension matrix, the table aims at: 1) Objectify one organization's position regarding its transformation journey 2) Depending on the organization, identify the specific improvement areas and suggest their sequence. An instantiation of the maturity model is also proposed through the case of the Indonesian red cross.
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Aditya Irfansyah, Adam Widera, Mark Haselkorn, & Bernd Hellingrath. (2020). Current Trends and Future Challenges in Congestion 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. 622–636). Blacksburg, VA (USA): Virginia Tech.
Abstract: Traffic congestion creates multidimensional impacts that require stakeholders' integration and coordination. This paper tries to close the research gaps in congestion management by examining a case study of integrated solutions of congestion measures and analyzing future challenges in congestion management based on two selected factors. The authors develop the result from the literature study and an expert interview that provides a better perspective on the case study. The study generates a new perspective on reviewing the organizational aspect of integrated congestion management measures. Secondly, it starts a discussion on future challenges in congestion management and connects the domain of future mobility with congestion theories as an independent discussion.
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Alexander Staves, Harry Balderstone, Benjamin Green, Antonios Gouglidis, & David Hutchison. (2020). A Framework to Support ICS Cyber Incident Response and Recovery. 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. 638–651). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the past decade there has been a steady increase in cyber attacks targeting Critical National Infrastructure. In order to better protect against an ever-expanding threat landscape, governments, standards bodies, and a plethora of industry experts have produced relevant guidance for operators in response to incidents. However, in a context where safety, reliability, and availability are key, combined with the industrial nature of operational systems, advice on the right practice remains a challenge. This is further compounded by the volume of available guidance, raising questions on where operators should start, which guidance set should be followed, and how confidence in the adopted approach can be established. In this paper, an analysis of existing guidance with a focus on cyber incident response and recovery is provided. From this, a work in progress framework is posited, to better support operators in the development of response and recovery operations.
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Stefan Schauer, Stefan Rass, Sandra König, Klaus Steinnocher, Thomas Schaberreiter, & Gerald Quirchmayr. (2020). Cross-Domain Risk Analysis to Strengthen City Resilience: the ODYSSEUS Approach. 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. 652–662). Blacksburg, VA (USA): Virginia Tech.
Abstract: In this article, we want to present the concept for a risk management approach to assess the condition of critical infrastructure networks within metropolitan areas, their interdependencies among each other and the potential cascading effects. In contrast to existing solutions, this concept aims at providing a holistic view on the variety of interconnected networks within a city and the complex dependencies among them. Therefore, stochastic models and simulations are integrated into risk management to improve the assessment of cascading effects and support decision makers in crisis situations. This holistic view will allow risk managers at the city administration as well as emergency organizations to understand the full consequences of an incident and plan mitigation actions accordingly. Additionally, the approach will help to further strengthen the resilience of the entire city as well as the individual critical infrastructures in crisis situations.
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Andrew Marinik, Ludwig Gantner, Scott Fritz, & Sean Smith. (2020). Developing Performance Metrics of an Emergency Notification System. 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. 663–668). Blacksburg, VA (USA): Virginia Tech.
Abstract: The use of emergency notification systems (ENS), or early warning systems, are not only common practice among Institutes of Higher Education (IHEs), but are required by law in the United States. The dramatic increase in use is matched by the increase in community expectation. This community expectation corresponding with societal shifts challenges Public Safety leaders to implement and maintain a broad and highly reliable ENS. Most Public Safety programs lack the internal resources to consistently assess system risk, reliability, and messaging validity of their ENS sufficient to match the required system performance. Virginia Tech Emergency Management is proposing an ENS evaluation system capable of supporting assessment of reliability and risk across the entire system through the lens of Socio-Technical Systems (STS) theory at a practitioner level. By organizing emergency notification/early warning systems through Human Subsystems, Technical Subsystems, and Task Design the practitioner can assess their system by performance and risk.
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Justin Michael Crow. (2020). Verifying Baselines for Crisis Event Information Classification on Twitter. 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. 670–687). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media are rich information sources during crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained to assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it's rare that code, trained models, or exhaustive parametrisation details are openly available. Thus, even confirming self-reported performance is problematic; authoritatively determining state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper makes 3 contributions: 1) replication and results confirmation of a leading technique; 2) testing straightforward modifications likely to improve performance; and 3) extension to a novel complimentary type of crisis-relevant information to demonstrate it's generalisability.
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Valerio Lorini, Javier Rando, Diego Saez-Trumper, & Carlos Castillo. (2020). Uneven Coverage of Natural Disasters in Wikipedia: The Case of Floods. 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. 688–703). Blacksburg, VA (USA): Virginia Tech.
Abstract: The usage of non-authoritative data for disaster management provides timely information that might not be available through other means. Wikipedia, a collaboratively-produced encyclopedia, includes in-depth information about many natural disasters, and its editors are particularly good at adding information in real-time as a crisis unfolds. In this study, we focus on the most comprehensive version of Wikipedia, the English one. Wikipedia offers good coverage of disasters, particularly those having a large number of fatalities. However, by performing automatic content analysis at a global scale, we also show how the coverage of floods in Wikipedia is skewed towards rich, English-speaking countries, in particular the US and Canada. We also note how coverage of floods in countries with the lowest income is substantially lower than the coverage of floods in middle-income countries. These results have implications for analysts and systems using Wikipedia as an information source about disasters.
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Hannah Van Wyk, & Kate Starbird. (2020). Analyzing Social Media Data to Understand How Disaster-Affected Individuals Adapt to Disaster-Related Telecommunications Disruptions. 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. 704–717). Blacksburg, VA (USA): Virginia Tech.
Abstract: Information is a critical need during disasters such as hurricanes. Increasingly, people are relying upon cellular and internet-based technology to communicate that information--modalities that are acutely vulnerable to the disruptions to telecommunication infrastructure that are common during disasters. Focusing on Hurricane Maria (2017) and its long-term impacts on Puerto Rico, this research examines how people affected by severe and sustained disruptions to telecommunications services adapt to those disruptions. Leveraging social media trace data as a window into the real-time activities of people who were actively adapting, we use a primarily qualitative approach to identify and characterize how people changed their telecommunications practices and routines--and especially how they changed their locations--to access Wi-Fi and cellular service in the weeks and months after the hurricane. These findings have implications for researchers seeking to better understand human responses to disasters and responders seeking to identify strategies to support affected populations.
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James A. Reep, & Andrea Tapia. (2020). Toward an Organizational Technology Adoption Process (OTAP) for Social Media Integration in a PSAP. 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. 718–729). Blacksburg, VA (USA): Virginia Tech.
Abstract: Integration of social media in emergency response environments presents specific organizational challenges, such as lack of resources or information credibility. Additionally, there exists individual resistance to change in these environments that could potentially discourage adoption. To identify and understand these challenges, we conducted semi-structured group interviews with emergency call takers and dispatchers. We find that these PSAP operators desire participation and explanation of changes throughout the organizational change process. Participants also articulated they desired training regarding change even when not directly affected. Though change management procedures often call for these strategies, they are commonly overlooked, leaving individuals to imagine worse case scenarios that manifest as additional stress in an already stressful work environment. It is suggested that a formalized change management process which directly addresses the identified challenges within the organizational technology adoption process (OTAP) is needed in order to mitigate undue stress.
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Lise Ann St. Denis, Amanda Lee Hughes, Jeremy Diaz, Kylen Solvik, Maxwell B. Joseph, & Jennifer K. Balch. (2020). 'What I Need to Know is What I Don't Know!': Filtering Disaster Twitter Data for Information from Local Individuals. 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. 730–743). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the design, development, and evaluation of a user labeling framework for social media monitoring by emergency responders. By labeling Twitter user accounts based on behavior and content, this novel approach identifies tweets from accounts belonging to Individuals generating Personalized content and captures information that might otherwise be missed. We evaluate the framework using training data from the 2018 Camp, Woolsey, and Hill fires. Approximately 30% of the Individual-Personalized tweets contain first-hand information, providing a rich stream of content for social media monitoring. Because it can quickly eliminate most redundant tweets, this framework could be a critical first step in an end-to-end information extraction pipeline. It may also generalize more easily for new disaster events since it relies on general user account attributes rather than tweet content. We conclude with next steps for refining and evaluating our framework in near real-time during a disaster response.
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Richard McCreadie, Cody Buntain, & Ian Soboroff. (2020). Incident Streams 2019: Actionable Insights and How to Find Them. 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. 744–760). Blacksburg, VA (USA): Virginia Tech.
Abstract: The ubiquity of mobile internet-enabled devices combined with wide-spread social media use during emergencies is posing new challenges for response personnel. In particular, service operators are now expected to monitor these online channels to extract actionable insights and answer questions from the public. A lack of adequate tools makes this monitoring impractical at the scale of many emergencies. The TREC Incident Streams (TREC-IS) track drives research into solving this technology gap by bringing together academia and industry to develop techniques for extracting actionable insights from social media streams during emergencies. This paper covers the second year of TREC-IS, hosted in 2019 with two editions, 2019-A and 2019-B, contributing 12 new events and approximately 20,000 new tweets across 25 information categories, with 15 research groups participating across the world. This paper provides an overview of these new editions, actionable insights from data labelling, and the automated techniques employed by participant systems that appear most effective.
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Muhammad Imran, Firoj Alam, Umair Qazi, Steve Peterson, & Ferda Ofli. (2020). Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence. 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. 761–773). Blacksburg, VA (USA): Virginia Tech.
Abstract: Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
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Jeremy Diaz, Lise St. Denis, Maxwell B. Joseph, Kylen Solvik, & Jennifer K. Balch. (2020). Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach? 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. 774–789). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer.
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Sandrine Bubendorff, & Caroline Rizza. (2020). The Wikipedia Contribution to Social Resilience During Terrorist Attacks. 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. 790–801). Blacksburg, VA (USA): Virginia Tech.
Abstract: This paper aims at studying the role of Wikipedia in social resilience processes during terrorist attacks. It discusses how Wikipedia users' specific skills are mobilized in order to make sense of the event as it unfolds. We have conducted an ethnographic analysis of several Wikipedia's terrorist attacks pages as well as interviews with regular Wikipedia's contributors. We document how Wikipedia is used during crisis by readers and contributors. Doing so, we identify a specific pace of contributions which provides reliable information to readers. By discussing the conditions of their trustworthiness, we highlight how historical sources (i.e. traditional media and authorities) support this pace. Our analyses demonstrate that citizens are engaging very quickly in processes of resilience and should be, therefore, considered as relevant partners by authorities when engaging a response to the crisis.
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Ferda Ofli, Firoj Alam, & Muhammad Imran. (2020). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. 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. 802–811). Blacksburg, VA (USA): Virginia Tech.
Abstract: Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
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Kamol Roy, MD Ashraf Ahmed, Samiul Hasan, & Arif Mohaimin Sadri, P. D. (2020). Dynamics of Crisis Communications in Social Media: Spatio-temporal and Text-based Comparative Analyses of Twitter Data from Hurricanes Irma and Michael. 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. 812–824). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media platforms play critical roles in information dissemination, communication and co-ordination during different phases of natural disasters as it is crucial to know the type of crisis information being disseminated and user concerns. Large-scale Twitter data from hurricanes Irma (Sept. 2017) and Michael (Oct. 2018) are used here to understand the topic dynamics over time by applying the Dynamic Topic Model, followed by a comparative analyses of the differences in such dynamics for these two hurricane scenarios. We performed a spatio-temporal analyses of user activities with reference to the hurricane center location and wind speed. The findings of spatio-temporal analyses show that differences in hurricane path and the affected regions influence user participation and social media activity. Besides, topic dynamics reveals that situational awareness, disruptions, relief action are among the patterns common for both hurricanes; unlike topics such as hurricane evacuation and political situation that are scenario dependent.
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Haiyan Hao, & Yan Wang. (2020). Hurricane Damage Assessment with Multi-, Crowd-Sourced Image Data: A Case Study of Hurricane Irma in the City of Miami. 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. 825–837). Blacksburg, VA (USA): Virginia Tech.
Abstract: The massive crowdsourced data generated on social networking platforms (e.g. Twitter and Flickr) provide free, real-time data for damage assessment (DA) even during catastrophes. Recent studies leveraging crowdsourced data for DA mainly focused on analyzing textual formats. Crowdsourced images can provide rich and objective information about damage conditions, however, are rarely researched for DA purposes. The highly-varied content and loosely-defined damage forms make it difficult to process and analyze the crowdsourced images. To address this problem, we propose a data-driven DA method based on multi-, crowd-sourced images, which includes five machine learning classifiers organized in a hierarchical structure. The method is validated with a case study investigating the damage condition of the City of Miami caused by Hurricane Irma. The outcome is then compared with a metric derived from NFIP insurance claims data. The proposed method offers a resource for rapid DA that supplements conventional DA methods.
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Liuqing Li, & Edward A. Fox. (2020). Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian. 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. 838–848). Blacksburg, VA (USA): Virginia Tech.
Abstract: We conducted a case study analysis of disaster response patterns across different user groups during Hurricane Dorian in 2019. We built a tweet collection about the hurricane, covering a two week period. We divided Twitter users into two groups: brand/organization or individual. We found a significant difference in response patterns between the groups. Brand users increasingly participated as the disaster unfolded, and they posted more tweets than individual users on average. Regarding emotions, brand users posted more tweets with joy and surprise, while individual users posted more tweets with sadness. Fear was a common emotion between the two groups. Further, both groups used different types of hashtags and words in their tweets. Some distinct patterns were also discovered in their concerns on specific topics. These results suggest the value of further exploration with more tweet collections, considering the behavior of different user groups during disasters.
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Rob Grace. (2020). Hyperlocal Toponym Usage in Storm-Related Social Media. 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. 849–859). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis responders need to locate events reported in social media messages that typically lack geographic metadata such as geotags. Toponyms, places names referenced in messages, provide another source of geographic information, however, the availability and granularity of toponyms in crisis social media remain poorly understood. This study examines toponym usage and granularity across six categories of crisis-related information posted on Twitter during a severe storm. Findings show users often include geographic information in messages describing local and remote storm events but do so rarely when discussing other topics, more often use toponyms than geotags when describing local events, and tend to include fine-grained toponyms in reports of infrastructure damage and service disruption and course-grained toponyms in other kinds of storm-related messages. These findings present requirements for hyperlocal geoparsing techniques and suggest that social media monitoring presents more immediate affordances for course-grained damage assessment than fine-grained situational awareness during a crisis.
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