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Zou, H. P., Caragea, C., Zhou, Y., & Caragea, D. (2023). Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 385–395). Omaha, USA: University of Nebraska at Omaha.
Abstract: The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.
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Zijun Long, & Richard Mccreadie. (2021). Automated Crisis Content Categorization for COVID-19 Tweet Streams. 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. 667–678). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.
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Zijun Long, & Richard McCreadie. (2022). Is Multi-Modal Data Key for Crisis Content Categorization on Social Media? In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1068–1080). Tarbes, France.
Abstract: The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail.
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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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Zeno Franco, Katinka Hooyer, Tanvir Roushan, Casey O'Brien, Nadiyah Johnson, Bill Watson, et al. (2018). Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 874–885). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Designing mHealth applications for mental health interventions has largely focused on education and patient self-management. Next generation applications must take on more complex tasks, including sensor-based detection of crisis events, search for individualized early warning signs, and support for crisis intervention. This project examines approaches to integrating multiple worn sensors to detect mental health crisis events in US military veterans. Our work has highlighted several practical and theoretical problems with applying technology to evaluation crises in human system, which are often subtle and difficult to detect, as compared to technological or natural crisis events. Humans often do not recognize when they are in crisis and under-report crises to prevent reputational damage. The current project explores preliminary use of the E4 Empatica wristband to characterize acute aggression using a combination of veteran self-report data on anger, professional actors simulating aggressive events, and preliminary efforts to discriminate between crisis data and early warning sign data.
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Zeno Franco, Katinka Hooyer, Rizwana Rizia, A B M Kowser Patwary, Mathew Armstrong, Bryan Semaan, et al. (2016). Dryhootch Quick Reaction Force: Collaborative Information Design to Prevent Crisis in Military Veterans. 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: Crises range from global catastrophes to personal disasters. However, systematic inquiry on crises rarely employs a comparative approach to examine commonalities between these seemingly very different events. We argue here that individual psychosocial disasters can inform a broader discussion on crises. Our approach applies general crisis theory to a smartphone based psychosocial support system for US military veterans. We engaged in a process designed to explore how veteran peer-to-peer mentorship can be augmented with IS support to display potential early warning signs as first step toward preventative intervention for high risk behaviors. To gain a better understanding of how military veterans might benefit from such a system, this article focuses on a community collaborative design process. The co-design process used the Small Stories method, allowing important cultural characteristics of to emerge, illuminating considerations in IS design with military veterans, and highlighting how humans think about crisis events at the individual level.
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Završnik, J., Vošner, H. B. žun, & Kokol, P. (2023). Pandemic crisis management: The EU project STAMINA. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1070). Omaha, USA: University of Nebraska at Omaha.
Abstract: Pandemics, as COVID-19 showed, can have the potential to result in serious global health threats and crises. Management of such kind of crisis presents a serious challenge due to the number of affected people, differences in legal, administrative, health procedures, political cultures, and the lack of smart interconnected, and compatible digitalized software tolls. The aim of the STAMINA project, sponsored by EU, was to overcome the above challenges and support efficient and effective pandemic management by providing Artificial intelligence-based decision-support technology which could successfully operate at a regional, national, and global level. The project targeted three stages of the emergency management cycle: Prediction, Preparedness, and Response. The STAMINA solution provides national planners, regional crisis management agencies, first responders, and citizens with new tools as well as a clear guide to how they can be used in line with international standards and legislation.
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Yu, X., Chen, J., & Liu, J. (2023). Examining the influence of social media on individual’s protective action taking during Covid-19 in China. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 295–308). Omaha, USA: University of Nebraska at Omaha.
Abstract: In the context of COVID-19, this study utilizes the Social Mediated Crisis Communication Model (SMCC) and the Protective Action Decision Model (PADM) to investigate the relationship between social media users' protective actions and crisis information during public health crises in China. By constructing a structural equation model, this study aims to identify the influencing factors that affect social media users' personal’s cognitive, emotional, and behavioral reactions given crisis relevant information. Results findings are that warning information can significantly increase risk perception; emotional responses are not significantly affected by warning information and risk perception; risk perception has a negative impact on information gathering and sharing behavior; risk perception has a significant mediating effect on the relationship between information features and protective action.
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Yongzhong Sha, Jinsong Yan, & Guoray Cai. (2014). Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog. 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. 722–726). University Park, PA: The Pennsylvania State University.
Abstract: Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events.
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Yohann Chasseray, Anne-Marie Barthe-Delanoë, Stéphane Négny, & Jean-Marc Le Lann. (2021). Automated unsupervised ontology population system applied to crisis management domain. 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. 968–981). Blacksburg, VA (USA): Virginia Tech.
Abstract: As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster.
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Yan, S. (2005). Design of enterprise crisis predicting system based on cluster and outlier data mining. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 143–145). Brussels: Royal Flemish Academy of Belgium.
Abstract: In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on cluster and outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it is a new way to solve such problems.
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Yan Song. (2006). Crisis detection in enterprises based on AHP with clustering. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 24–29). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Crisis detection can help enterprises to make full preparation to respond real crisis, so it is an important field to promote enterprises' competition and keep them develop continuously. Crisis in enterprises may be caused by many factors and most of them are very common and necessary parts in normal operating procedure. This paper takes these parts as crisis signals indicated in many managing books. Group decision-making strategy is put forward to help enterprises to analyze crisis signals based on the characteristics of the decision-making procedure. To get a meaningful and credible result, AHP is used to support the whole procedure. To exhibit the role of managers, system cluster is used to classify experts involved in decision-making procedure. An example to analyze a key engineer's dismissing is given to illustrate the decision-making procedure and to prove the efficiency of this idea and AHP method.
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Yajie Li, Amanda Lee Hughes, & Peter D. Howe. (2018). Communicating Crisis with Persuasion: Examining Official Twitter Messages on Heat Hazards. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 469–479). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Official crisis messages need to be persuasive to promote appropriate public responses. However, little research has examined the content of crisis messages from a persuasion perspective, especially for natural hazards. This study deductively identifies five persuasive message factors (PMFs) applicable to natural hazards, including two under-examined health-related PMFs: health risk susceptibility and health impact. Using 2016 heat hazards as a case study, this paper content-analyzes heat-related Twitter messages (N=904) posted by eighteen U.S. National Weather Service Weather Forecast Offices according to the five PMFs. We find that the use of descriptions of hazard intensity is disproportionately high, with a lack of use of other PMFs. We also describe different types of statements used to signal the two health-related PMFs. We conclude with implications and recommendations relevant to practitioners and researchers in social media crisis communication.
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Xiaoyong Ni, Hong Huang, Wenxuan Dong, Chao Chen, Boni Su, & Anying Chen. (2021). Scenario Prediction and Crisis Management for Rain-induced Waterlogging Based on High-precision Simulation. 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. 159–173). Blacksburg, VA (USA): Virginia Tech.
Abstract: Many cities, especially those in developing countries, are not well prepared for the devastating disaster of exceptional rain-induced waterlogging caused by extreme rainfall. This paper proposes a waterlogging scenario prediction and crisis management method for such kind of extreme rainfall conditions based on high-precision waterlogging simulation. A typical urban region in Beijing, China is selected as the study area in this paper. High-precision and full-scale data in the study area requested for the waterlogging simulation are introduced. The simulation results show that the study area is still vulnerable to extreme rainfall and the subsequent waterlogging. The waterlogging situation is much more severe with the increase of the return period of rainfall. This study offers a good reference for the relevant government departments to make effective policy and take pointed response to the waterlogging problem.
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Xiaodan Yu, & Deepak Khazanchi. (2015). Patterns of Information Technology (IT) Adaptation in Building Shared Mental Models for Crisis Management Teams. 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: One of the essential tasks of crisis management is to develop shared mental models (SMM) among teams and members about the crisis at hand, i.e. shared understanding of the task, process, technology and the teams. This is essential for developing an effective crisis management strategy. In this paper we draw lessons from our studies of distributed teams and their adaptation of IT capabilities to impact shared understanding. In particular, we discuss how patterns of the interplay between IT adaptation and SMM development have implications for crisis management teams.
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Xiaodan Yu, & Deepak Khazanchi. (2019). The Influence of Swift Trust on Virtual Team's Sensemaking in Crisis: A Research Model. 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: Virtual teams are an important form of collaboration, especially in the context of transboundary crises. Achieving
team effectiveness through good sensemaking is key to virtual teams? success in responding to crisis. However,
there is still a lack of understanding about the relationship of this sensemaking in a virtual team during crisis to
the virtual team?s development of swift trust. Adapting from a model of sensemaking, in this paper, we propose a
research model to describe the relationships among swift trust, sensemaking and virtual team performance in the
context of virtual teams during crisis.
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Xiaodan Yu, & Deepak Khazanchi. (2017). Studying Virtual Teams during Organizational Crisis from a Sociomaterial Perspective. 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 (1055). Albi, France: Iscram.
Abstract: In this paper, we propose sociomaterialism as a theoretical lens for studying virtual team management during organizational crisis. In applying this lens, we propose the use of pattern theory as the method of choice for documenting effective practices for managing virtual teams in organizational crisis settings.
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William R. Smith, Keri K. Stephens, Brett Robertson, Jing Li, & Dhiraj Murthy. (2018). Social Media in Citizen-Led Disaster Response: Rescuer Roles, Coordination Challenges, and Untapped Potential. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 639–648). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Widespread disasters can overload official agencies' capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.
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Willem Van Santen, Catholijn M. Jonker, & Niek Wijngaards. (2009). Crisis decision making through a shared integrative negotiation mental model. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Decision making during crises takes place in (multi-agency) teams, in a bureaucratic political context. As a result, the common notion that during crises decision making should be done in line with a Command & Control structure is invalid. This paper shows that the best way for crisis decision making teams in a bureaucratic political context is to follow an integrative negotiation approach as the shared mental model of decision making. This conclusion is based on an analysis of crisis decision making by teams in a bureaucratic political context. First of all this explains why in a bureaucratic political context the Command & Control adage does not hold. Secondly, this paper motivates why crisis decision making in such context can be seen as a negotiation process. Further analysis of the given context shows that an assertive and cooperative approach suits crisis decision making best.
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Willem Treurniet, Kim Van Buul-Besseling, & Jeroen Wolbers. (2012). Collaboration awareness – A necessity in crisis response coordination. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: In crisis management involvement of a large number of organizations is required. Not only the first responders need to take action, but also organizations and entities like civil authorities, public utility and crisis teams are responsible for critical infrastructures as well as the community. A key element for effective collaboration is situation awareness; having a common operational picture. So far research has mainly focused on situation awareness. However, several incidents show that situation awareness alone is not sufficient for reaching effective collaboration among the organizations involved. Collaboration awareness is a second key element. Knowing the needs, goals, expectations, culture, capabilities and procedures of the crisis management partners makes collaboration more effective. In this paper we elaborate our research focusing on what organizations need to know about each other in order to collaborate effectively. Finally, we describe the possible measures for increasing the collaboration awareness. © 2012 ISCRAM.
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Willem J. Muhren, Gerd Van Den Eede, & Bartel A. Van De Walle. (2008). Sensemaking as a methodology for ISCRAM research: Information processing in an ongoing crisis. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 315–323). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper attempts to reveal the “black box” of information processing activities by relying on Sensemaking as a methodology and as the object of research. In particular, this research aims at studying intuitive information processing activities in ongoing crisis situations, one of the most extreme contexts in which discontinuity is the rule and continuity the exception. The authors argue that this Sensemaking approach offers valuable insights for the design of information systems for crisis response and management (ISCRAM). This paper describes an interpretive case study methodology as it was applied to discover Sensemaking episodes in the daily work of humanitarian relief actors in the ongoing crisis of the Democratic Republic of Congo.
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Willem J. Muhren, Damir Durbic, & Bartel A. Van De Walle. (2010). Exploring decision-relevant information pooling by humanitarian disaster response teams. 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: It is a well-known fact that a lack of information will lead to suboptimal decisions. But even when actors jointly have all the information they need to make a well-informed decision, they may fail to find a superior alternative. This hidden profile paradigm would cause misrepresentations of crisis situations and lead to ineffective response. In this research-in-progress paper, we present the first stage of our experimental study on group decision making in humanitarian disaster response, in which we want to find out how teams can be supported to share more information, make better sense, and ultimately avoid such misrepresentations of crisis situations. First results reveal that humanitarian disaster response teams are able to share significantly more information if they would make use of more advanced information and communication systems. However, none of the teams in the experimental setup managed to find the optimal decision.
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Wang, D., & Kogan, M. (2023). Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 487–500). Omaha, USA: University of Nebraska at Omaha.
Abstract: Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses.
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Wang, D. (2023). Public Cognition and Perception on Social Media in Crisis. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1081–1082). Omaha, USA: University of Nebraska at Omaha.
Abstract: Microblogging platforms have been increasingly used in crisis, facilitating more participatory communication between official response channels and affected communities. Despite the potential benefits, research has shown that disaster response organizations could not effectively utilize social media data due to data deluge (Knox 2022). To better understand the information needed for disaster response, we turn to the National Incident Management System Guidance for public information officers (PIOs) (NIMS Basic Guidance for PIOs 2020), the primary spokesperson for emergency management organizations. The guidance indicates that PIOs use social media for two major purposes, supporting their operational needs and gauging public perception of risk and incident response. To support the operational needs, the crisis informatics literature has heavily focused on information types supporting situational awareness, including serviceable, eyewitness or actionable information. However, the information representing public perception, such as people’s cognitive and perceptual processes in response to incidents, has been less addressed at scale. To bridge the gap between quantitative study in crisis informatics and information representing cognitive and perceptual processes and better support the task of PIOs, I focus on the study of people’s cognitive and perceptual processes on social media for my research. Cognitive and perceptual processes refer to the way that people pay attention to or process environmental inputs, including the mental activities of acquisition, processing or evaluation of environmental cues, social cues, and warnings. These processes reveal people’s perception of- and decision-making in response to potential threats. With this focus, I seek to answer the following research question: How could people’s cognitive and perceptual processes be inferred from their social media activities in crisis to benefit stakeholders in incident response? My interest in tracing this overall theme through a varied range of sub-tasks produces three more specific research questions: RQ1. How can information exposure and attention be operationalized to highlight cognitive and perceptual processes? RQ2. How do people’s perception of risk communications from stakeholders vary in crisis? RQ3. How could a principled and scalable pipeline be designed to identify people’s cognitive and affective perceptions on Twitter? I took cues from the Protective Action Decision Model (Lindell and Perry 2012) and leveraged baselines in the literature to address these research questions. To address the first research question, I proposed a metric that conceptualized and operationalized the predecision process. The proposed metric was incorporated into a pipeline and applied to two real-word events to recommend messages that represent the shift of collective attention of those locally affected with a specialized focus on cognitive and perceptual processes. To address the second research question, I went beyond the perception of risks to include perceptions of risk communications by stakeholders. I performed an empirical study of the relation between risk communications by stakeholders and different kinds of public perceptions (Lindell and Perry 2012). To address the third research question, I proposed a future work to provide benchmark coding schemes, datasets and models to quantitatively identify information representing cognitive and perceptual processes. I will leverage existing benchmark datasets in the literature (Olteanu et al. 2014; Imran et al. 2016; Alam et al. 2018; Zahra et al. 2020; Rudra et al. 2017; Mazloom et al. 2018; Purohit et al. 2018) and coding schemes in qualitative studies (Trumbo et al. 2016; Demuth et al. 2018) and create benchmark classification models.
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Viveca Asproth, Erik A.M. Borglund, & Lena-Maria Öberg. (2013). Exercises for crisis management training in intra-organizational settings. 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. 105–109). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In this article the focus is how to train collaboration and communication between emergency authorities in two countries (Norway and Sweden) by using a web-based tool supporting tabletop like exercises. The exercises are accomplished in three steps: Scenario design; exercise design and realization; and evaluation to examine the results of the exercises and for feedback to new scenario designs. The software ties all three steps together. The process is iterative, and involves users from each emergency authority. The preliminary results after two years show that the approach is promising. To be able to better foresee what will happen during an exercise the need for a simulator has appeared as one desirable and possible direction for further research.
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