Rahman, S., Ramakrishnan, T., & Ngamassi, L. (2023). Social Media Use for Disaster Management by Underserved Communities: A Uses and Gratification Theory Perspective. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1074). Omaha, USA: University of Nebraska at Omaha.
Abstract: Social media has emerged as a useful disaster management tool. However, studies indicate that not all individuals are equally inclined towards using social media for managing disasters. Underserved communities have not been able to reap the benefits of social media for disaster management to its full potential. We draw on the Uses and Gratification Theory and the literature on disaster vulnerability of underserved communities to develop a conceptual model. In our poster, we make five propositions in order to examine the motivating factors for the underserved communities to use social media for disaster management.
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Lucia Castro Herrera, & Terje Gjøsæter. (2022). Community Segmentation and Inclusive Social Media Listening. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1012–1023). Tarbes, France.
Abstract: Social media analytics provide a generalized picture of situational awareness from the conversations happening among communities present in social media channels that are that are, or risk being affected by crises. The generalized nature of results from these analytics leaves underrepresented communities in the background. When considering social media analytics, concerns, sentiment, and needs are perceived as homogenous. However, offline, the community is diverse, often segmented by age group, occupation, or language, to name a few. Through our analysis of interviews from professionals using social media as a source of information in public service organizations, we argue that practitioners might not be perceiving this segmentation from the social media conversation. In addition, practitioners who are aware of this limitation, agree that there is room for improvement and resort to alternative mechanisms to understand, reach, and provide services to these communities in need. Thus, we analyze current perceptions and activities around segmentation and provide suggestions that could inform the design of social media analytics tools that support inclusive public services for all, including persons with disabilities and from other disadvantaged groups.
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Savannah Thais, Shaine Leibowitz, Allie Saizan, & Ashay Singh. (2022). Understanding Historical, Socio-Economic, and Policy Contributions to COVID-19 Health Inequities. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 481–494). Tarbes, France.
Abstract: The COVID-19 pandemic has generated unprecedented, devastating impacts across the United States. However, some communities have disproportionately endured adverse health outcomes and socioeconomic injuries. Ascertaining the factors driving these inequities is crucial to determining how policy could mitigate the impacts of future public health crises. We have established research-driven metrics, aggregated as the Community Vulnerability Index (CVI), that quantify vulnerability to public health and economic impacts of COVID-19. We performed two analyses to better understand similarities between communities in terms of the vulnerabilities represented by the metrics. We performed an unsupervised k-means clustering analysis to understand whether communities can be grouped together based on their levels of negative social and health indicators. Our goal for this analysis is to determine whether attributes of the constructed clusters reveal areas of opportunity for potential policy impacts and future disaster response efforts. We also analyzed similarities between communities across time using time-sensitive clustering analysis to discover whether historical community vulnerabilities were persistent in the years preceding the pandemic and to better understand the historical factors associated with disparate COVID-19 impacts. In particular, we highlight where communities should invest based on their historical health and socioeconomic patterns and related COVID impacts. Through extensive interpretation of our findings, we uncover how health policy can advance equity and improve community resilience.
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Thomas Papadimos, Nick Pantelidis, Stelios Andreadis, Aristeidis Bozas, Ilias Gialampoukidis, Stefanos Vrochidis, et al. (2022). Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 623–635). Tarbes, France.
Abstract: The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.
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Vangelis Pitidis, Joao Porto de Albuquerque, Jon Coaffee, & Fernanda Lima. (2022). Enhancing Community Resilience through Dialogical Participatory Mapping. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 495–503). Tarbes, France.
Abstract: Citizen generated data can play an important role in enhancing community resilience. However, the relationship between data and community resilience has only been partly addressed in existing resilience scholarship, predominantly from the perspective of data utilisation in response to unfolding crises. Yet, in this study we attempt to highlight a different pathway for data-enabled contributions to community resilience, focusing on the process of data generation and its capacity to constitute a transformative moment itself. By exploring the case of the marginalized flood-prone community of M’Boi Mirim in São Paulo, Brazil, we introduce the concept of dialogical participatory mapping, and we argue that the process of generating geospatial data can empower local communities and assist in nourishing a resilience spirit among community members.
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