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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Gkika, I., Pattas, D., Konstantoudakis, K., & Zarpalas, D. (2023). Object detection and augmented reality annotations for increased situational awareness in light smoke conditions. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 231–241). Omaha, USA: University of Nebraska at Omaha.
Abstract: Innovative technologies powered by Computer Vision algorithms can aid first responders, increasing their situ ational awareness. However, adverse conditions, such as smoke, can reduce the efficacy of such algorithms by degrading the input images. This paper presents a pipeline of image de-smoking, object detection, and augmented reality display that aims to enhance situational awareness in smoky conditions. A novel smoke-reducing deep learning algorithm is applied as a preprocessing step, before state-of-the-art object detection. The detected objects and persons are highlighted in the user’s augmented reality display. The proposed method is shown to increase detection accuracy and confidence. Testing in realistic environments provides an initial evaluation of the method, both in terms of image processing and of usefulness to first responders.
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Paulini, M. S., Duran, D., Rice, M., Andrekanic, A., & Suri, N. (2023). KENNEL Threat Detection Boxes for First Responder Situational Awareness and Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 208–219). Omaha, USA: University of Nebraska at Omaha.
Abstract: KENNEL is a deployable IoT-based system consisting of a network of unattended ground sensors, known as Threat Detection Boxes (TDBs), which may be outfitted with any variety of custom and commercial-off-the-shelf sensors for hazard detection. The KENNEL system fills a technological gap for sensor fusion, interpretation, and real-time alerting via existing information management systems, such as Team Awareness Kit (TAK). First responders face a critical need for improved situational awareness, detection, and response to hazardous events. KENNEL provides a first of its kind, low-cost sensing & data fusion platform that is highly extensible, configurable, and self-sustaining, opening a world of modernization and innovation possibilities across the first responder domain. TDBs may also be statically or ad hoc deployed, improving flexibility, stand-off hazard detection, and resilience in the operational domain. From critical infrastructure monitoring to wearables, the system affords timeliness of critical information for effective risk management and increased personnel safety.
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Holzhüter, M., Huhle, G., Reuter-Oppermann, M., Hellriegel, J., & Klafft, M. (2023). Acceptance study on application systems to improve situational incident management through bi-directional communication between citizens and decision-makers in emergencies and crises situations. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 197–207). Omaha, USA: University of Nebraska at Omaha.
Abstract: Efficient hazard prevention and disaster control depend on situational awareness. Situational information is – among others – provided by citizens on the ground. Disaster managers are often reluctant to use such information on a large scale or in a systematic way for fear of being overwhelmed by information overload in a stressful crisis. New information technologies for crisis management are strongly dependent on the acceptance of the people using them and can only be successful as socio-technical systems. Therefore, 354 employees of public and private emergency operation centres as well as members of crisis management teams were asked to assess different information sharing technologies. 504 people from the public responded to an online survey about their willingness to use such technologies. The results indicate a high level of acceptance by both user groups for bi directional communication technologies for situation management and the improvement of situational awareness.
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Ly Dinh, Sumeet Kulkarni, Pingjing Yang, & Jana Diesner. (2023). Reliability of Methods for Extracting Collaboration Networks from Crisis-related Situational Reports and Tweets. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 181–195). Palmerston North, New Zealand: Massey Unversity.
Abstract: Assessing the effectiveness of crisis response is key to improving preparedness and adapting policies. One method for response evaluation is reviewing actual response activities and interactions. Response reports are often available in the form of natural language text data. Analyzing a large number of such reports requires automated or semi automated solutions. To improve the trustworthiness of methods for this purpose, we empirically validate the reliability of three relation extraction methods that we used to construct interorganizational collaboration networks by comparing them against human-annotated ground truth (crisis-specific situational reports and tweets). For entity extraction, we find that using a combination of two off-the-shelf methods (FlairNLP and SpaCy) is optimal for situational reports data and one method (SpaCy) for tweets data. For relation extraction, we find that a heuristics-based model that we built by leveraging word co-occurrence and deep and shallow syntax as features and training it on domain-specific text data outperforms two state-of-the-art relation extraction models (Stanford OpenIE and OneIE) that were pre-trained on general domain data. We also find that situational reports, on average, contain less entities and relations than tweets, but the extracted networks are more closely related to collaboration activities mentioned in the ground truth. As it is widely known that general domain tools might need adjustment to perform accurately in specific domains, we did not expect the tested off-the-shelf tools to perform highly accurately. Our point is to rather identify what accuracy one could reasonably expect when leveraging available resources as-is for domain specific work (in this case, crisis informatics), what errors (in terms of false positives and false negatives) to expect, and how to account for that.
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