Sérgio Freire, Aneta Florczyk, & Stefano Ferri. (2015). Modeling Day- and Nighttime Population Exposure at High Resolution: Application to Volcanic Risk Assessment in Campi Flegrei. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Improving analyses of population exposure to potential natural hazards, especially sudden ones, requires more detailed geodemographic data. Availability of such information for large areas is limited by specific database requirements and their cost.
This paper introduces and tests a new approach for refining spatio-temporal population distribution at high resolution by combining diverse geoinformation layers. Its value is demonstrated in the context of disaster risk analysis and emergency management by using the data in a real volcanic risk scenario in Campi Flegrei, located within the metropolitan area of Naples, Italy. Results show that there is significant variation in exposure from nighttime to daytime in the study area.
The proposed modeling approach can be applied and customized for other metropolitan areas, ultimately benefiting disaster risk assessment and mitigation.
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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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Nancy Shank, Brian Sokol, Michelle Hayes, & Cristina Vetrano. (2008). Human services data standards: Current progress and future vision in crisis response. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 352–361). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Interorganizational coordination is crucial among human services providers responsible for responding to both personal and widespread crises. Too often, however, agencies providing disaster relief, shelter, and connection to other social service systems operate in information silos. Moreover, organizations that assist the same people may be duplicating services or ineffectively providing services to those in need. In the past, there has been no easy way for human service organizations to share information about clients, resources, and services. Over the last decade, distinct initiatives have begun to standardize data collection, storage, and transmission standards within human service domains. This paper describes several human services standards currently in use or under development in the United States and discusses how each support distinct, yet related, human service information management during disasters. The paper concludes with a call for the development of an overarching human services data interoperability standard.
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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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Songhui Yue, Jyothsna Kondari, Aibek Musaev, Songqing Yue, & Randy Smith. (2018). Using Twitter Data to Determine Hurricane Category: An Experiment. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 718–726). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
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Steve Peterson, Keri Stephens, Hemant Purohit, & Amanda Hughes. (2019). When Official Systems Overload: A Framework for Finding Social Media Calls for Help during Evacuations. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: During large-scale disasters it is not uncommon for Public Safety Answering Points (e.g., 9-1-1) to encounter
service disruptions or become overloaded due to call volume. As observed in the two past United States hurricane
seasons, citizens are increasingly turning to social media whether as a consequence of their inability to reach
9-1-1, or as a preferential means of communications. Relying on past research that has examined social media
use in disasters, combined with the practical knowledge of the first-hand disaster response experiences, this paper
develops a knowledge-driven framework containing parameters useful in identifying patterns of shared
information on social media when citizens need help. This effort explores the feasibility of determining
differences, similarities, common themes, and time-specific discoveries of social media calls for help associated
with hurricane evacuations. At a future date, validation of this framework will be demonstrated using datasets
from multiple disasters. The results will lead to recommendations on how the framework can be modified to make
it applicable as a generic disaster-type characterization tool.
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Tsai, C. - H., Rayi, P., Kadire, S., Wang, Y. - F., Krafka, S., Zendejas, E., et al. (2023). Co-Design Disaster Management Chatbot with Indigenous Communities. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1–12). Omaha, USA: University of Nebraska at Omaha.
Abstract: Indigenous communities are disproportionately impacted by rising disaster risk, climate change, and environmental degradation due to their close relationship with the environment and its resources. Unfortunately, gathering the necessary information or evidence to request or co-share sufficient funds can be challenging for indigenous people and their lands. This paper aims to co-design an AI-based chatbot with two tribes and investigate their perception and experience of using it in disaster reporting practices. The study was conducted in two stages. Firstly, we interviewed experienced first-line emergency managers and invited tribal members to an in-person design workshop. Secondly, based on qualitative analysis, we identified three themes of emergency communication, documentation, and user experience. Our findings support that indigenous communities favored the proposed Emergency Reporter chatbot solution. We further discussed how the proposed chatbot could empower the tribes in disaster management, preserve sovereignty, and seek support from other agencies.
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Venkata Kishore Neppalli, Murilo Cerqueira Medeiros, Cornelia Caragea, Doina Caragea, Andrea Tapia, & Shane Halse. (2016). Retweetability Analysis and Prediction during Hurricane Sandy. 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: Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.
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Hayley Watson, & Rachel L. Finn. (2013). Privacy and ethical implications of the use of social media during a volcanic eruption: Some initial thoughts. 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. 416–420). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In a relatively new area of research for crisis management, this working paper presents a preliminary discussion of some of the privacy and ethical implications surrounding the use of social media in the event of a crisis. The paper uses the chaos caused by the eruptions of the Eyjafjallajokull volcano in 2010 to contextualise the analysis. It begins by presenting two case studies of the use of social media by members of the public and the aviation industry during the crisis caused by the ash plume. The paper then proceeds to briefly highlight some select ethical and privacy implications stemming from the use of social media such as privacy infringements and inequality. The paper concludes by briefly summarising the findings of the paper and considering next steps for future research in this area.
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Yang Zhang, William Drake, Yuhong Li, Christopher Zobel, & Margaret Cowell. (2015). Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience.
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Sarp Yeletaysi, Frank Fiedrich, & John R. Harrald. (2008). A framework for integrating GIS and systems simulation to analyze operational continuity of the petroleum supply chain. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 586–595). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Crisis and disaster management is a field that requires the understanding and application of tools and knowledge from multiple disciplines. Hurricanes Katrina and Rita in 2005 have proven that U.S. petroleum infrastructure is vulnerable to major supply disruptions as a direct result of disasters. Due to the structure of U.S. oil supply chain, primary oil production centers (i.e. PADD* 3) are geographically separated from primary demand centers (i.e. PADD 1), which creates a natural dependency between those districts. To better understand the extent of those dependencies and downstream impacts of supply disruptions, a multi-disciplinary research approach is necessary. The cross-disciplines in this research include disaster management, critical infrastructure and oil supply chain management, and the utilization of geographic information systems (GIS) and systems simulation. This paper specifically focuses on the framework for integrating GIS and systems simulation as analysis tools in this research.
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