Lindsley G. Boiney, Bradley Goodman, Robert Gaimari, Jeffrey Zarrella, Christopher Berube, & Janet Hitzeman. (2008). Taming multiple chat room collaboration: Real-time visual cues to social networks and emerging threads. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 660–668). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Distributed teams increasingly rely on collaboration environments, typically including chat, to link diverse experts for real time information sharing and decision-making. Current chat-based technologies enable easy exchange of information, but don't focus on managing those information exchanges. Important cues that guide face-to-face collaboration are either lost or missing. In some military environments, operators may juggle over a dozen chat rooms in order to collaborate on complex missions. This often leads to confusion, overload, miscommunication and delayed decisions. Our technology supports chat management. A summary display bar reduces the number of chat rooms operators need open by providing high level situational awareness pointers, in real-time, to: a) rooms with increasing message activity levels, b) rooms in which important collaborators are participating (those in the operator's social network), and c) rooms in which operator-selected keywords are used. This ability to peripherally monitor less critical chat rooms reduces operator overload, while enhancing the ability to rapidly detect important emerging discussion threads. © 2008 The MITRE Corporation. All rights reserved.
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Jill L. Drury, Gary L. Klein, Mark Pfaff, & Steven O. Entezari. (2012). Establishing collaborative option awareness during crisis management. 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: This paper presents empirical results of the use of a novel decision support prototype for emergency response situations, which was designed to enhance the understanding of the relative desirability of one potential course of action versus another. We have termed this understanding “option awareness.” In particular, this paper describes the process employed by pairs of experiment participants while performing emergency responder roles using different types of “decision space” visualizations to help them collaborate on decisions. We examined the decision making process via a detailed analysis of the communication between the cooperating team members. The results yield implications for design approaches for visualizing option awareness. © 2012 ISCRAM.
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Hussain A. Syed, Marén Schorch, & Volkmar Pipek. (2020). Disaster Learning Aid: A Chatbot Centric Approach for Improved Organizational Disaster Resilience. 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. 448–457). Blacksburg, VA (USA): Virginia Tech.
Abstract: The increasingly frequent occurrence of organizational crises exemplifies the need to strengthen organizational resilience. An example of business organizations is small and medium enterprises (SMEs) which contribute largely to the economic growth. But often, their limited resources (manpower, time, financial capital), organizational structure, focus on operational routines and less priority towards disaster resilience make them more vulnerable to crisis than bigger companies. The proposed solution addresses this dilemma by establishing a collaborative medium within the organization to improve disaster resilience by raising awareness and self-learning in employees without overburdening their constrained routines and resources. Our work in progress demonstrates a conceptual model of a learning aid (collaboration channel and a chatbot) that supports the pedagogical methodologies and employs them for enhancing learnability and awareness and elaborates the usability of interactive learning instilling disaster resilience in employees and hence in an organization.
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Li, H., Caragea, D., Mhatre, A., Ge, J., & Liu, M. (2023). Identifying COVID-19 Tweets Relevant to Low-Income Households Using Semi-supervised BERT and Zero-shot ChatGPT Models. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 953–963). Omaha, USA: University of Nebraska at Omaha.
Abstract: Understanding the COVID-19 pandemic impacts on low-income households can inform social services about the needs of vulnerable communities. Some recent works have studied such impacts through social media content analysis, and supervised machine learning models have been proposed to automatically classify COVID-19 tweets into different categories, such as income and economy impacts, social inequality and justice issues, etc. In this paper, we propose semi-supervised learning models based on BERT with Self-Training and Knowledge Distillation for identifying COVID-19 tweets relevant to low-income households by leveraging readily available unlabeled data in addition to limited amounts of labeled data. Furthermore, we explore ChatGPT’s potential for annotating COVID-19 data and the performance of fine-tuned GPT-3 models. Our semi-supervised BERT model with Knowledge Distillation showed improvements compared to a supervised baseline model, while zero-shot ChatGPT showed good potential as a tool for annotating crisis data. However, our study suggests that the cost of fine-tuning large and expensive GPT-3 models may not be worth for some tasks.
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Sophie Gerstmann, Hans Betke, & Stefan Sackmann. (2019). Towards Automated Individual Communication for Coordination of Spontaneous Volunteers. 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: In recent years, spontaneous volunteers often turned out to be a critical factor to overcome disaster situations and
avoid further damages to life and assets. These Volunteers coordinate their activities using social media and
mobile devices but are not integrated in usual command and control structures of disaster responders. The lack of
professional disaster response knowledge leads to a waste of potential workforce or even dangerous situations for
the volunteers. In this paper, a novel approach for a centralized coordination of spontaneous volunteers through
disaster response professionals while using popular communication channels esp. messaging services (e.g.
Facebook Messenger, WhatsApp) is presented. The architecture of a volunteer coordination system focusing on
automated multi-channel communication is shown and the possibilities of a universal chatbot for individual
assignment and scheduling of volunteers are discussed. The paper also provides first insights in a demonstrator
system as a practical solution.
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Tsai, C. - H., Kadire, S., Sreeramdas, T., VanOrmer, M., Thoene, M., Hanson, C., et al. (2023). Generating Personalized Pregnancy Nutrition Recommendations with GPT-Powered AI Chatbot. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 263–271). Omaha, USA: University of Nebraska at Omaha.
Abstract: Low socioeconomic status (SES) and inadequate nutrition during pregnancy are linked to health disparities and adverse outcomes, including an increased risk of preterm birth, low birth weight, and intrauterine growth restriction. AI-powered computational agents have enormous potential to address this challenge by providing nutrition guidelines or advice to patients with different health literacy and demographics. This paper presents our preliminary exploration of creating a GPT-powered AI chatbot called NutritionBot and investigates the implications for pregnancy nutrition recommendations. We used a user-centered design approach to define the target user persona and collaborated with medical professionals to co-design the chatbot. We integrated our proposed chatbot with ChatGPT to generate pregnancy nutrition recommendations tailored to patients’ lifestyles. Our contributions include introducing a design persona of a pregnant woman from an underserved population, co-designing a nutrition advice chatbot with healthcare experts, and sharing design implications for future GPT-based nutrition chatbots based on our preliminary findings.
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