Briony Gray, Mark Weal, & David Martin. (2018). Building Resilience in Small Island Developing States: Social Media during the 2017 Atlantic Hurricane Season. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 469–479). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: There are growing concerns that future Atlantic hurricane seasons will be severe and unpredictable due to underlying factors such as climate change. The 2017 season may offer a range of lessons, especially to small island developing states (SIDS), who are looking to build community resilience and heighten community engagement to cope with disaster. While many SIDS utilise a range of media and technology for these purposes, there has been a recent uptake in the use of social media, which may have further potential to support their goals. This paper scopes the use and users of social media in the case of Antigua and Barbuda during the 2017 Atlantic hurricane season. Through a series of qualitative interviews it explains the role that social media currently has, and concludes with suggestions for its improvement in future seasons that are contextualized over the disaster lifecycle phases.
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Briony Gray, Mark Weal, & David Martin. (2018). Supporting Situational Awareness during Disasters: The Case of Hurricane Irma. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 123–131). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: In a rapidly globalizing world, disasters and the way in which they are managed are changing. Social media, in conjunction with other online resources, now provide a wealth of information throughout the lifecycle of disasters and are relied upon by individuals and emergency responders alike. The study of such data as a lens for analysis has proved valuable in recent years, with many contributing to targeted emergency response protocols and improved methods for the management strategies of future crises. This study seeks to make a similar contribution by reporting on the use of such data for situational awareness during the case of hurricane Irma, which occurred between September and August 2017. Using a mixed methods approach the paper examines data from social media such as Twitter, as well as other online sources such as blogs and news media, to provide original insight into the disaster. A conceptual framework is then applied to determine the uses and users of social media, and to identify how these change throughout the course of the disaster, thus demonstrating situational awareness over time. The paper concludes with proposed improvements for disaster management and emergency response for future similar disasters, specifically in the hurricane season, in addition to more generalized hazards which are predicted to increase in their frequency and severity due to underlying issues such as climate change.
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Ana C. Calderon, Joanne Hinds, & Peter Johnson. (2014). IntCris: A tool for enhanced communication and collective decision-making during crises. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 205–214). University Park, PA: The Pennsylvania State University.
Abstract: Responding to a large-scale disaster such as an earthquake or hurricane is a collective problem. Human agents are increasingly collaborating with non-human agents (autonomous systems) in attempt to respond to a disaster. IntCris is a prototype intended to bring together interaction for human and non-human agents to aid the decision-making process by focusing on how to facilitate the “correct information to the correct agent” problem as well as encouraging new and agile behaviour. We focus on three categories of information: command, report and personal with a formal grammar to accompany the implementation. The requirements for the software were inspired by real life case studies from Hurricane Katrina, the Fukoshima Nuclear Disaster and Hurricane Sandy. The contribution of this work is to advance technology that brings together HAS (human and autonomous system interaction), in addition to enhancing collective intelligence.
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Cornelia Caragea, Anna Squicciarini, Sam Stehle, Kishore Neppalli, & Andrea H. Tapia. (2014). Mapping moods: Geo-mapped sentiment analysis during hurricane sandy. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 642–651). University Park, PA: The Pennsylvania State University.
Abstract: Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster.
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Chanvi Kotak, Brian Tomaszewski, & Erik Golen. (2018). 3-1-1 Calls Hot Spot Analysis During Hurricane Harvey: Preliminary Results. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 350–361). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Hurricane Harvey caused massive damage and necessitated the need for identification of areas under high risk. During Harvey, the city of Houston received more than 77000, 3-1-1 calls for assistance. Due to damage caused to the infrastructure, it became difficult to handle and respond to the crisis. Geographic Information Systems (GIS) is a vital technology to assist with real-time disaster monitoring. we investigated if a correlation could be found between 311 data calls made during the Hurricane Harvey and aerial images captured during the event, specifically to see if 311 data could be ground-truthed via hot spot analysis. Preliminary results indicate that visual representation of 3-1-1 call data can aid in analyzing the expected areas of high traffic of calls for assistance and plan an effective way to manage resources. Future work will involve more in-depth analysis of combined 3-1-1 call data with satellite imagery using image classification techniques.
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Tina Comes, & Bartel A. Van De Walle. (2014). Measuring disaster resilience: The impact of hurricane sandy on critical infrastructure systems. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 195–204). University Park, PA: The Pennsylvania State University.
Abstract: Modern critical infrastructure (CI) systems are tightly coupled, resulting in unprecedented complexity and difficulty to predict, limit and control the consequences of disruptions caused by hazards. Therefore, a paradigm shift in disaster risk management is needed: instead of focusing on predicting events, resilience needs to be improved as a basis for adequate response to any event. This paper starts from a definition of CI resilience that provides a basis for quantitative and qualitative decision support. For the quantitative modelling approach, which aims at measuring the resilience of individual CIs, we focus on two CIs of fundamental importance for disaster response: transportation and power supply. The qualitative framework details relations between CIs. The results of this research are illustrated by a case study that analyses the impact of Hurricane Sandy. The findings highlight the need for a framework that combines qualitative and quantitative information from heterogeneous sources to improve disaster resilience.
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Tom De Groeve, Luca Vernaccini, & Alessandro Annunziato. (2006). Modelling disaster impact for the global disaster alert and coordination system. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 409–417). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: The Global Disaster Alert and Coordination System, jointly developed by the European Commission and the United Nations, combines existing web-based disaster information management systems with the aim to alert the international community in case of major sudden-onset disasters and to facilitate the coordination of international response during the relief phase of the disaster. The disaster alerts are based on automatic hazard information retrieval and real-time running of impact models. This paper describes impact models for earthquakes, tsunamis and tropical cyclones.
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Derya Ipek Eroglu, Duygu Pamukcu, Laura Szczyrba, & Yang Zhang. (2020). Analyzing and Contextualizing Social Vulnerability to Natural Disasters in Puerto Rico. 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. 389–395). Blacksburg, VA (USA): Virginia Tech.
Abstract: As the third hurricane the U.S. experienced in 2017, Hurricane María generated impacts that resulted in both short term and long term suffering in Puerto Rico. In this study, we aim to quantify the vulnerability of Puerto Ricans by taking region and society specific characteristics of the island into account. To do this, we follow Cutter et al.'s social vulnerability calculation, which is an inductive approach that aims to represent a society based on its characteristics. We adapted the Social Vulnerability Index (SoVI) for Puerto Rico by using data obtained from the U.S. Census Bureau. We analyzed the newly calculated SoVI for Puerto Rico and compared it with the existing deductive approach developed by the Center for Disease Control (CDC). Our findings show that the new index is able to capture some characteristics that the existing vulnerability index is unable to do.
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Gary M. Fetter, & Mauro Falasca. (2011). Establishing the need for decision support in disaster debris disposal. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: One of the most important and costly aspects of recovery operations is debris collection and disposal. The unique nature of disaster debris and the extreme amounts generated as a result of the disaster event create challenges for decision makers that are not typically encountered during every day solid-waste disposal operations. This work-in-progress research is aimed at identifying the unique aspects of disaster debris disposal and the need for decision support, which addresses these unique aspects, to assist emergency management coordinators with allocating resources during on-going debris cleanup operations. We will present a decision support system framework, discuss aspects of the knowledge base, model base, and user interface, and show how an emergency management coordinator might use the system during ongoing daily operations using real-world data from a 2003 Atlantic hurricane.
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Gary M. Fetter, Mauro Falasca, Christopher W. Zobel, & Terry R. Rakes. (2010). A multi-stage decision model for debris disposal operations. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: As shown by Hurricane Katrina, disposing of disaster-generated debris can be quite challenging. Extraordinary amounts of debris far exceeding typical annual amounts of solid waste are almost instantaneously deposited across a widespread area. Although the locations and amounts of debris can be easily summarized looking back after recovery activities have been completed, they are uncertain and difficult at best to estimate as debris operations begin to unfold. Further complicating matters is that the capacity of cleanup resources, which is dependent upon available equipment, labor, and subcontractors, can fluctuate during on-going cleanup operations. As a result, debris coordinators often modify initial resource assignments as more accurate debris estimates and more stable resource capacities become known. In this research, we develop a computer-based decision support system that incorporates a multi-stage programming model to assist decision makers with allocating debris cleanup resources immediately following a crisis event and during ongoing operations as debris volumes and resource capacities become known with increasing certainty.
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Jennings Anderson, Marina Kogan, Melissa Bica, Leysia Palen, Kenneth Anderson, Rebecca Morss, et al. (2016). Far Far Away in Far Rockaway: Responses to Risks and Impacts during Hurricane Sandy through First-Person Social Media Narratives. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: When Hurricane Sandy swept over the US eastern seaboard in October 2012, it was the most tweeted about event at the time. However, some of the most affected areas were underrepresented in the social media conversation about Sandy. Here, we examine the hurricane-related experiences and behaviors shared on Twitter by residents of Far Rockaway, a New York City neighborhood that is geographically and socioeconomically vulnerable to disasters, which was significantly affected by the storm. By carefully filtering the vast Twitter data, we focus on 41 Far Rockaway residents who offer rich personal accounts of their experience with Sandy. Analyzing their first-person narratives, we see risk perception and protective decision-making behavior in their data. We also find themes of invisibility and neglect when residents expressed feeling abandoned by the media, the city government, and the overall relief efforts in the aftermath of Sandy.
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Satria Hutomo Jihan, & Aviv Segev. (2013). Context ontology for humanitarian assistance in crisis response. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 526–535). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Massive crisis open data is not fully utilized to identify humanitarian needs because most of it is not in a structured format, thus hindering machines to interpret it automatically and process it in a short time into useful information for decision makers. To address these problems, the paper presents a method which merges ontologies and logic rules to represent the humanitarian needs and recommend appropriate humanitarian responses. The main advantage of the method is to identify humanitarian needs and to prioritize humanitarian responses automatically so that the decision makers are not overwhelmed with massive and unrelated information and can focus more on implementing the solutions. The method is implemented on real data from the Hurricane Wilma crisis. The use of the method in the hurricane Wilma crisis shows the potential abilities to identify the humanitarian needs in specific places and to prioritize humanitarian responses in real time.
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Laura Szczyrba, Yang Zhang, Duygu Pamukcu, & Derya Ipek Eroglu. (2020). A Machine Learning Method to Quantify the Role of Vulnerability in Hurricane Damage. 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. 179–187). Blacksburg, VA (USA): Virginia Tech.
Abstract: Accurate pre-disaster damage predictions and post-disaster damage assessments are challenging because of the complicated interrelationships between multiple damage drivers, including various natural hazards, as well as antecedent infrastructure quality and demographic characteristics. Ensemble decision trees, a family of machine learning algorithms, are well suited to quantify the role of social vulnerability in disaster impacts because they provide interpretable measures of variable importance for predictions. Our research explores the utility of an ensemble decision tree algorithm, Random Forest Regression, for quantifying the role of vulnerability with a case study of Hurricane Mar\'ia. The contributing predictive power of eight drivers of structural damage was calculated as the decrease in model mean squared error. A measure of social vulnerability was found to be the model's leading predictor of damage patterns. An additional algorithm, other methods of quantifying variable importance, and future work are discussed.
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Michael K. Lindell. (2011). Evacuation modelling: Algorithms, assumptions, and data. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Survey researchers need to, Find out what assumptions evacuation modelers are making and collect empirical data to replace incorrect assumptions;, Obtain data on the costs of evacuation to households, businesses, and local government; and, Extend their analyses to address the logistics of evacuation and the process of re-entry. Evacuation modelers need to, Incorporate available empirical data on household evacuation behavior, and, Generate estimates of the uncertainties in their analyses. Cognitive scientists need to, Conduct experiments on hurricane tracking and evacuation decision making to better understand these processes, and, Develop training programs, information displays, and performance aids to assist local officials who have little or no previous experience in hurricane evacuation decision making.
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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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Michael Erskine, Scott Seipel, & Cayson Seipel. (2022). Development of a Geospatial Agent-Based Simulation of Disaster Evacuations for Battery Electric Vehicle (BEV) Policy. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 532–540). Tarbes, France.
Abstract: Several nations have signaled their intent to phase out petroleum-based engines for passenger vehicles and promote a transition to battery electric vehicles (BEVs). While researchers have established the long-term environmental benefits of BEVs, there are critical considerations for policymakers in areas prone to natural disasters. This research intends to develop a geospatial-based model to explore and simulate the evacuation of BEVs during a disaster. This work-in-progress (WiPe) paper examines the variables essential to creating an effective hurricane simulation. The final simulation model is intended to allow for the evaluation of BEV policy options under a variety of scenarios. We describe the considerations made during the development of this geospatial agent-based simulation under various hurricane parameters. Finally, we mention the expected benefits of our work and hint at possible policy directions.
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Tim Murphy, & Murray E. Jennex. (2006). Knowledge management systems developed for hurricane katrina response. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 615–624). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: This paper explores the use of knowledge management with emergency information systems. Two knowledge management systems that were utilized during Hurricane Katrina response are described and analyzed. The systems specified were developed by both federal agencies as well as grass root efforts without the support or mandate of government programs. These programs, although developed independently, were able to share data and interact in life saving capacities, transcending traditional geo-political boundaries. We conclude that emergency information systems are enhanced by incorporating knowledge management tools and concepts.
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Murray Turoff, Victor A. Bañuls, & Miguel Ramirez de la Huerga. (2018). Hurricanes Send Signals for the Future of Emergency Preparedness. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 797–805). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Trends over the past decades when coupled with recent disaster events call into serious question whether our typical reactions to natural disasters will be sufficient for what we can expect in the future. This paper summarizes current events and scientific understanding of our planet to provide insights of the authors into what should be the basis for future policies and plans.
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Rachel Samuels, John Eric Taylor, & Neda Mohammadi. (2018). The Sound of Silence: Exploring How Decreases in Tweets Contribute to Local Crisis Identification. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 696–704). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Recent research has identified a correlation between increasing Twitter activity and incurred damage in disasters. This research, however, fails to account for localized emergencies occurring in areas in which people have lost power, otherwise lack internet connectivity, or are uncompelled to Tweet during a disaster. In this paper, we analyze the correlation between daily Tweet counts and FEMA Building Level Damage Assessments during Hurricane Harvey. We find that the absolute deviation of Tweet counts from steady state is a potentially useful tool for the evolving information needs of emergency responders. Our results show this to be a more consistent and persistent metric for flood damage across the full temporal extent of the disaster. This shows that, when considering the varied information needs of emergency responders, social media tools that seek to identify emergencies need to consider both where Tweet counts are increasing and where they are dropping off.
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Robin Batard, Frederick Benaben, Aurélie Montarnal, Christophe Prieur, & Caroline Rizza. (2018). Ethical, Legal and Social considerations surrounding the use of social media by citizens during Hurricane Irma in Cuba. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 268–275). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: While Hurricane Irma struck the Cuban Southern coasts, thousands of tourists were evacuated from this area and relocated in the Varadero peninsula. In a couple of days, all means for families and friends to connect with the tourists were down, leaving them without any information about the on-site situation. This paper focuses on the volunteer citizens' initiative to take advantage of social media, to enhance their own situational awareness in Varadero area, supporting fellow citizens to identify and localize their relatives. In particular, two Facebook groups that were created at the time are analyzed and their messages' content and objectives categorized. We will show that once more, social media has constituted opportunities for citizens to engage a specific response to the crisis, but at the same time has raised specific ethical and social issues.
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Shalini Priya, Manish Bhanu, Sourav Kumar Dandapat, & Joydeep Chandra. (2021). Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 609–620). Blacksburg, VA (USA): Virginia Tech.
Abstract: A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art.
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Shane Errol Halse, Andrea Tapia, Anna Squicciarini, & Cornelia Caragea. (2016). An Emotional Step Towards Automated Trust Detection in Crisis Social Media. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the effects of perceived emotion of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, we examine perceived emotions of these messages and how the different emotions affect the perceived usefulness and trustworthiness. Our analysis is carried out on two datasets gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a significant difference in the perceived emotions that contribute towards the perceived trustworthiness and usefulness. This could have impacts on how messages from social media data are analyzed for use in crisis response.
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Shane Errol Halse, Andrea Tapia, Anna Squicciarini, & Cornelia Caragea. (2016). Tweet Factors Influencing Trust and Usefulness During Both Man-Made and Natural Disasters. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, in this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features. Our analysis is carried out on two datasets (one natural and one man made) gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a high correlation and similar factors (support for the victims, informational data, use of humor and type of emotion used) influencing trustworthiness and usefulness for both disaster types. This could have impacts on how messages from social media data are analyzed for use in crisis response.
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Shane Halse, Jess Kropczynski, & Andrea Tapia. (2018). Using Metrics of Stability to Identify Points of Failure and Support in Online Information Distribution during a Disaster. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (p. 1121). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: We utilize the 2012 Hurricane Sandy dataset to investigate methods to measure network stability during a crisis. While previous research on information distribution has focused on individuals that are most connected, or most willing to share information, we examined this dataset for indicators of network stability. The value of this measure is to identify the points of failure within the network. The findings in this paper provide support for the use of social network analysis within the realm of crisis response to identify the points of failure within the network.
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Alexander Smirnov, Mikhail Pashkin, Nikolay Shilov, & Tatiana Levashova. (2007). Intelligent support of context-based megadisaster management: Hybrid technology and case study. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 305–316). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The situation with the hurricane Katrina showed that the conventional tiered response to disaster event, whereby state and local officials are responsible for the first few days, does not work well in case of megadisasters (massive hurricanes, earthquakes, large-scale acts of terrorism, etc.). Such situations require application of new technologies for preparing the operation, interoperability between the operation participants, and decision support for officials. Here presented approach proposes a context-driven decision support schema based on integration of such technologies as context & ontology management and constraint satisfaction. The application of the approach is illustrated via a case study of a portable hospital arrangement.
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