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1. Analyzing Social Media Data to Understand How Disaster-Affected Individuals Adapt to Disaster-Related Telecommunications Disruptions

   Van Wyk, Hannah ; Starbird, Kate ( May 2020 , CoRe Paper – Social Media for Disaster Response and Resilience Proceedings of the 17th ISCRAM Conference)

   McNeill, Fiona ; Zobel, Christopher (Ed.)

   Information is a critical need during disasters such as hurricanes. Increasingly, people are relying upon cellular and internet-based technology to communicate that information—modalities that are acutely vulnerable to the disruptions to telecommunication infrastructure that are common during disasters. Focusing on Hurricane Maria (2017) and its long-term impacts on Puerto Rico, this research examines how people affected by severe and sustained disruptions to telecommunications services adapt to those disruptions. Leveraging social media trace data as a window into the real-time activities of people who were actively adapting, we use a primarily qualitative approach to identify and characterize how people changed their telecommunications practices and routines—and especially how they changed their locations—to access Wi-Fi and cellular service in the weeks and months after the hurricane. These findings have implications for researchers seeking to better understand human responses to disasters and responders seeking to identify strategies to support affected populations. 

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2. A different type of disaster response digital volunteer: Looking behind the scenes to reveal coordinating actions of disaster knowledge workers

   https://doi.org/10.1111/1468-5973.12352  

   Smith, William Roth ; W. Robertson, Brett ; K. Stephens, Keri ; Murthy, Dhiraj ( January 2021 , Journal of Contingencies and Crisis Management)

   Researchers have established the prominent role digital volunteers play during crises and disasters. From self‐organizing to annotating public data, these volunteers are now a fixture in disaster research. However, we know much less about how these volunteers function, behind the public scene, when using private social media as a disaster unfolds and people need to be rescued. This qualitative study identified the emergent helping roles along with the skillsets and abilities that helped volunteers perform these behind‐the‐scene roles during the Hurricane Harvey flooding in 2017. Using in‐depth interviews along with captured images in private social media, we find these volunteers resembled organizational knowledge workers. We identify nine specific communicating and coordinating actions that these disaster knowledge workers performed. The contributions of these findings centre on implications for disaster response and management.

    

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3. Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective

   https://doi.org/10.1007/s11069-018-3260-9  

   Ulak, Mehmet B. ; Kocatepe, A. ; Konila Sriram, Lalitha M. ; Ozguven, Eren E. ; and Arghandeh, R. ( July 2018 , Natural hazards)

   Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capital of Florida. The regional and socioeconomic variations in the Hermine’s impact were studied via spatially and statistically analyzing power outages. First step includes a spatial analysis to illustrate the magnitude of customers affected by power outages together with a clustering analysis. This step aims to determine whether the customers affected from outages are clustered or not. Second step involves a Bayesian spatial autoregressive model in order to identify the effects of several demographic-, socioeconomic-, and transportation-related variables on the magnitude of customers affected by power outages. Results showed that customers affected by outages are spatially clustered at particular regions rather than being dispersed. This indicates the need to pinpoint such vulnerable locations and develop strategies to reduce hurricane-induced disruptions. Furthermore, the increase in the magnitude of affected customers was found to be associated with several variables such as the power network and total generated trips as well as the demographic factors. The information gained from the findings of this study can assist emergency officials in identifying critical and/or less resilient regions, and determining those demographic and socioeconomic groups which were relatively more affected by the consequences of hurricanes than others. 

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4. Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters

   https://doi.org/10.1111/mice.12576  

   Zhang, Cheng ; Yao, Wenlin ; Yang, Yang ; Huang, Ruihong ; Mostafavi, Ali ( June 2020 , Computer-Aided Civil and Infrastructure Engineering)

   Understanding the societal impacts caused by community disruptions (e.g., power outages and road closures), particularly during the response stage, with timeliness and sufficient detail is an underexplored, yet important, consideration. It is critical for effective decision‐making and coordination in disaster response and relief activities as well as post‐disaster virtual reconnaissance activities. This study proposes a semiautomated social media analytics approach for social sensing of Disaster Impacts and Societal Considerations (SocialDISC). This approach addresses two limitations of existing social media analytics approaches: lacking adaptability to the need of different analyzers or different disasters and missing the information related to subjective feelings, emotions, and opinions of the people. SocialDISC labels and clusters social media posts in each disruption category to facilitate scanning by analyzers. Analyzers, in this paper, are persons who acquire social impact information from social media data (e.g., infrastructure management personnel, volunteers, researchers from academia, and some residents impacted by the disaster). Furthermore, SocialDISC enables analyzers to quickly parse topics and emotion signals of each subevent to assess the societal impacts caused by disruption events. To demonstrate the performance of SocialDISC, the authors proposed a case study based on Hurricane Harvey, one of the costliest disasters in U.S. history, and analyzed the disruptions and corresponding societal impacts in different aspects. The analysis result shows that Houstonians suffered greatly from flooded houses, lack of access to food and water, and power outages. SocialDISC can foster an understanding of the relationship between disruptions of infrastructures and societal impacts, expectations of the public when facing disasters, and infrastructure interdependency and cascading failures. SocialDISC's provision of timely information about the societal impacts of people may help disaster response decision‐making.

    

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5. Multimodal Semi-supervised Learning for Disaster Tweet Classification

   Iustin Sirbu, Tiberiu Sosea ( October 2022 , The 29th International Conference on Computational Linguistics (COLING 2022))

   During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at https://github.com/iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification. 

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