Keys, PatrickMeyer, Alexis2021-11-022021-11-022021https://hdl.handle.net/10217/234030http://dx.doi.org/10.25675/10217/234030Metadata for the articles appearing in the literature corpus, corresponding to the table in the manuscript entitled: 'Table S1: Visions of the Arctic Future - Metadata for Arctic news corpus'.School of Global Environmental SustainabilityThe future of Arctic social systems and natural environments is highly uncertain. Climate change will lead to unprecedented phenomena in the pan-Arctic region, such as regular shipping traffic through the Arctic Ocean, urban growth, military activity, expanding agricultural frontiers, and transformed indigenous societies.While intergovernmental to local organizations have produced numerous synthesis-based visions of the future, a challenge in any scenario exercise is capturing the possibility space of change. In this work, we employ a computational text analysis to objectively generate unique thematic input for novel, story-based visions of the Arctic. Specifically, we develop a corpus of more than 2,000 articles in publicly accessible, English-language Arctic newspapers that discuss the future in the Arctic. We then perform a latent Dirichlet allocation, resulting in ten distinct topics and sets of associated keywords. From these topics and keywords, we design ten story-based scenarios employing the Mānoa mashup, science fiction prototyping, and other methods. Our results demonstrate that computational text analysis can feed directly into a creative futuring process, whereby the output stories can be traced clearly back to the objectively identified topics and keywords. We discuss our findings in the context of the broader field of Arctic scenarios, and show that the results of this computational text analysis produce complementary stories to the existing scenario literature. We conclude that story-based scenarios can provide vital texture toward understanding the myriad possible Arctic futures.CSVPDFengtext analysisclimate changeliterature corpusArctic scenarioslatent Dirichlet allocationstorytellingpolarDatasetThis material is open access and distributed under the terms and conditions of the Creative Commons CC BY-SA: Attribution-ShareAlike 4.0 International license (https://creativecommons.org/licenses/by-sa/4.0/).