摘要：Digitally-aided reviews of large bodies of text-based information, such as academic literature, are growing in capability but are not yet common in environmental fields. Environmental sciences and studies can benefit from application of digital tools to create comprehensive, replicable, interdisciplinary reviews that provide rapid, up-to-date, and policy-relevant reports of existing work. This work reviews the potential for applications of computational text mining and analysis tools originating in the humanities to environmental science and policy questions. Two process-oriented case studies of digitally-aided environmental literature reviews and meta-analyses illustrate potential benefits and limitations. A medium-sized, medium-resolution review (~8000 journal abstracts and titles) focuses on topic modeling as a rapid way to identify thematic changes over time. A small, high-resolution review (~300 full text journal articles) combines collocation and network analysis with manual coding to synthesize and question empirical field work. We note that even small digitally-aided analyses are close to the upper limit of what can be done manually. Established computational methods developed in humanities disciplines and refined by humanities and social science scholars to interrogate large bodies of textual data are applicable and useful in environmental sciences but have not yet been widely applied. Two case studies provide evidence that digital tools can enhance insight. Two major conclusions emerge. First, digital tools enable scholars to engage large literatures rapidly and, in some cases, more comprehensively than is possible manually. Digital tools can confirm manually identified patterns or identify additional patterns visible only at a large scale. Second, digital tools allow for more replicable and transparent conclusions to be drawn from literature reviews and meta-analyses. The methodological subfields of digital humanities and computational social sciences will likely continue to create innovative tools for analyzing large bodies of text, providing opportunities for interdisciplinary collaboration with the environmental fields.
