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Daniel P. Eriksson. (2006). A region-specific prognostic model of post-earthquake international attention. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 418–425). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: This project evaluates the feasibility of a prognostic model for international attention following earthquakes. The degree of international attention is defined as the number of situation reports issued by the United Nations. Ordinal regression is applied to a set of 58 case study events that occurred in Central Asia between 1992 and 2005. The context of the model is promising. Patterns were identified among the misclassified events. The patterns can prove helpful in understanding the irregular behavior of the international community and to improve future models by identifying subjects, such as bilateral relations and willingness to request external aid, for which additional indicators are needed.
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Charlotte Hellgren, & Björn J.E. Johansson. (2012). Reducing workload by navigational support in dynamic situations. 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: By presenting continuously updated heading and distance information on a small head-mounted display (HMD), as a supplement to a GPS-receiver, we examined if workload could be reduced and performance increased, when navigating in a demanding situation. The purpose was to present limited but sufficient information to facilitate navigation. The technique was tested on ground troops, but could also be used by rescue services and police in situations that require navigation in unknown environments. The main findings were that the workload was reduced in one aspect (during navigation) but increased in another (looking for foot placement). There were no clear differences in performance, except that participants stopped fewer times to look at the GPS-receiver if they had updated heading and distance information. This suggests that a supplement display with minimal information could be useful when navigating with a GPS-receiver in an unknown environment. © 2012 ISCRAM.
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John M. McGuirl, Nadine B. Sarter, & David D. Woods. (2008). Seeing is believing?: The effects of real-time, image-based feedback on emergency management decision-making. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 406–414). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Emergency management personnel often face feedback delays and a lack of reliable information. To address this problem, new information technologies have been developed that can provide real-time, image-based feedback. While potentially useful, this trend represents a fundamental shift in both the timing and format of the information used by incident commanders (ICs). Eight ICs took part in a simulation exercise to determine the potential impact of real-time imaging on their decision-making. Nearly all of the ICs failed to detect important changes in the situation that were not captured in the imaging but that were available via other, more traditional data sources. It appears that the ICs placed an inappropriately high level of trust in the imaging data, resulting in reduced data search activities and hypothesis generation. This research helps practitioners anticipate and guard against undesirable effects of introducing similar technologies on training and operational procedures.
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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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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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