Frontiers in data analytics for adaptation research: Topic modeling

Alexandra Lesnikowski*, Ella Belfer, Emma Rodman, Julie Smith, Robbert Biesbroek, John D. Wilkerson, James D. Ford, Lea Berrang-Ford

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)


Rapid growth over the past two decades in digitized textual information represents untapped potential for methodological innovations in the adaptation governance literature that draw on machine learning approaches already being applied in other areas of computational social sciences. This Focus Article explores the potential for text mining techniques, specifically topic modeling, to leverage this data for large-scale analysis of the content of adaptation policy documents. We provide an overview of the assumptions and procedures that underlie the use of topic modeling, and discuss key areas in the adaptation governance literature where topic modeling could provide valuable insights. We demonstrate the diversity of potential applications for topic modeling with two examples that examine: (a) how adaptation is being talked about by political leaders in United Nations Framework Convention on Climate Change; and (b) how adaptation is being discussed by decision-makers and public administrators in Canadian municipalities using documents collected from 25 city council archives. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation.

Original languageEnglish
Article numbere576
JournalWiley Interdisciplinary Reviews: Climate Change
Issue number3
Early online date27 Feb 2019
Publication statusPublished - Jun 2019


  • climate change adaptation
  • governance
  • policy
  • quantitative text analysis
  • topic models


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