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Ur?ka Demsar, Olga Patenková, & Kirsi Virrantaus. (2007). Centrality measures and vulnerability of spatial networks. 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. 201–209). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Effective management of infrastructural networks in the case of a crisis requires a prior analysis of the vulnerability of spatial networks and identification of critical locations where an interdiction would cause most damage and disruption. This paper presents a preliminary study into how a graph theoretic structural analysis could be used for this purpose. Centrality measures are combined with a dual graph modelling approach in order to identify critical locations in a spatial network. The results of a case study on a street network of a small area in the city of Helsinki indicate that 'betweenness' is the most promising centrality measure for this purpose. Other measures and properties of graphs are under consideration for eventually developing a risk model not only for one but for a group of co-located spatial networks.
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Zeno Franco, Syed Ahmed, Craig E. Kuziemsky, Paul A. Biedrzycki, & Anne Kissack. (2013). Using social network analysis to explore issues of latency, connectivity, interoperability & sustainability in community disaster 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. 896–900). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Community-based disaster response is gaining attention in the United States because of major problems with domestic disaster recovery over the last decade. A social network analysis approach is used to illustrate how community-academic partnerships offer one way to leverage information about existing, mediated relationships with the community through trusted actors. These partnerships offer a platform that can be used to provide entré into communities that are often closed to outsiders, while also allowing greater access to community embedded physical assets and human resources, thus facilitated more culturally appropriate crisis response. Using existing, publically available information about funded community-academic partnerships in Wisconsin, USA, we show how social network analysis of these meta-organizations may provide critical information about both community vulnerabilities in disaster and assist in rapidly identifying these community resources in the aftermath of a crisis event that may provide utility for boundary spanning crisis information systems.
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Lamsal, R., Read, M. R., & Karunasekera, S. (2023). A Twitter narrative of the COVID-19 pandemic in Australia. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 353–370). Omaha, USA: University of Nebraska at Omaha.
Abstract: Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.
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Nathan Elrod, Pranav Mahajan, Monica Katragadda, Shane Halse, & Jess Kropczynski. (2021). An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic. 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. 345–358). Blacksburg, VA (USA): Virginia Tech.
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.
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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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Yan Wang, & John E. Taylor. (2017). Tracking urban resilience to disasters: a mobility network-based approach. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 97–109). Albi, France: Iscram.
Abstract: Disaster resilience is gaining increasing attention from both industry and academia, but difficulties in operationalizing the concept remain, especially in the urban context. Currently, there is scant literature on measuring both spatial and temporal aspects of resilience empirically. We propose a bio-inspired quantitative framework to track urban resilience to disasters. This framework was built upon a daily human mobility network, which was generated by geolocations from a Twitter Streaming API. System-wide metrics were computed over time (i.e. pre-, during and post-disasters). Fisher information was further adopted to detect the perturbation and dynamics in the system. Specifically, we applied the proposed approach in a flood case in the metropolis of São Paulo. The proposed approach is efficient in uncovering the dynamics in human movements and the underlying spatial structure. It adds to our understanding of the resilience process in urban disasters.
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