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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 (January 2020, International Conference on Information Systems for Crisis Response and Management (ISCRAM 2020))

   Hughes, Amanda; McNeill, Fiona; Zobel, Christopher W. (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. 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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3. 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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4. Exploring sex differences in information needs, media dependencies, and attention allocation during Hurricane Dorian

   https://doi.org/10.5055/jem.0652  

   Lachlan, PhD; Spence, PhD; Hutter, MA; Gilbert, MA (September 2021, Journal of Emergency Management)

   In September 2019, Hurricane Dorian struck the Bahamas and the southeast United States, resulting in widespread damage and loss of life. Drawing from previous crisis communication research on both natural and man-made disasters, this study examines information seeking and medium preferences, attention allocation, and sex differences in these outcomes. Extant literature has found differences between men and women in terms of the volume and types of information wanted during a crisis event, as well as preferences for different media in times of crisis. This literature has yet to examine the degree to which attention allocation may be related to these outcomes. To address these issues in a naturalistic context, a large-scale survey was targeted at residents of states impacted by Hurricane Dorian. Results are consistent with previous research indicating that females engaged in more overall information seeking and sought more information seeking related to tangible goals. Females found interactive media (Internet and social media) to be more useful than males. Evidence was not detected concerning sex differences in the way people found out about the storm and sex differences in attention allocation detected. Results suggested small effects for perceived usefulness of television and Internet on attention allocation for both men and women. Implications for emergency management personnel and public officials are discussed. 

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5. Cascading disasters: The impact of hurricane Maria and Covid-19 on post-disaster Puerto Rican migrants’ adaptation and integration in Florida

   https://doi.org/10.1057/s41276-022-00390-3  

   Aranda, Elizabeth; Blackwell, Rebecca; Escue, Melanie; Rosa, Alessandra (December 2022, Latino Studies)

   Based on data from 103 surveys of Puerto Rican migrants living in Florida and 54 in-depth interviews with a subgroup of them, we examine how Puerto Ricans who left the archipelago after Hurricane Maria have navigated settlement in their new homes. In this article, we observed and classified our participants’ descriptions of how they managed opportunities and challenges regarding education, employment, and social relations, the traditional benchmarks for the assessment of societal integration. We also observed how our participants described Covid-19’s interaction with these benchmarks. We found that our participants have experienced a series of cascading disasters since 2017—namely, Hurricane Maria, the earthquakes that affected Puerto Rico starting in late 2019, the humanitarian crises that followed both disasters, and now the global pandemic. These disasters, compounded with migration, have resulted in a process of adaptation to Florida in which social and labor-market integration and the ability to nurture social ties have been significantly diminished. 

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