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Utilizing Qualitative Data for Social Network Analysis in Disaster Research: Opportunities, Challenges, and an Illustration.

Bailey C BenedictSeungyoon LeeCaitlyn M JarvisLaura K SiebeneckRachel Wolfe
Published in: Disasters (2023)
An abundance of unstructured and loosely structured data on disasters exists and can be analyzed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. We discuss two types of networks, each with a relevant major topic in disaster research (i.e., whole network approaches to emergency management networks and personal network approaches to the social support of survivors) and four usable forms of qualitative data. We explain five opportunities afforded by these approaches revolving around their flexibility and ability to account for complex network structures. Next, we present an empirical illustration that extends our previous work examining the sources and types of support and barrier experienced by households during long-term recovery from Superstorm Sandy, wherein we utilized quantitative social network analysis on two qualitative datasets (Lee et al., 2020). We discuss three challenges for these approaches related to the samples, coding, and bias.
Keyphrases
  • network analysis
  • social support
  • electronic health record
  • healthcare
  • big data
  • mental health
  • depressive symptoms
  • high resolution
  • public health
  • machine learning
  • young adults
  • drinking water
  • artificial intelligence