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Automated content analysis of the Hawaiʻi small boat fishery survey reveals nuanced, evolving conflicts

Aviv Suan, Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa
Kirsten M. Leong, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, National Oceanographic and Atmospheric Administration (NOAA)
Kirsten L.L. Oleson, Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa


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Manual content analysis provides a systematic and reliable method to analyze patterns within a narrative text, but for larger datasets, where human coding is not feasible, automated content analysis methods present enticing and time-efficient solutions to classifying patterns of text automatically. However, the massive dataset needed and complexity of analyzing these large datasets have hindered their use in fishery science. Fishery scientists typically deal with intermediately sized datasets that are not large enough to warrant the complexity of sophisticated automated techniques, but that are also not small enough to cost-effectively analyze by hand. For these cases, a dictionary-based automated content analysis technique can potentially simplify the automation process without losing contextual sensitivity. Here, we built and tested a fisheries-specific data dictionary to conduct an automated content analysis of open-ended responses in a survey of the Hawaiʻi small boat fishery to examine the nature of the fishery conflict. In this paper we describe the overall performance of the methodology, creating and applying the dictionary to fishery data, as well as advantages and limitations of the method. The results indicate that the dictionary approach is capable of quickly and accurately classifying unstructured fisheries data into structured data, and that it was useful in revealing deeply rooted conflicts that are often ambiguous and overlooked in fisheries management. In addition to providing a proof of concept for the approach, the dictionary can be reused on subsequent waves of the survey to continue monitoring the evolution of these conflicts. Further, this approach can be applied within the field of fishery and natural resource conservation science more broadly, offering a valuable addition to the methodological toolbox.

Key words

automated content analysis; codebook; conservation conflicts; fisheries; Hawaiʻi; text mining; qualitative coding

Copyright © 2021 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

Ecology and Society. ISSN: 1708-3087