Visions of the Arctic Future: Blending Computational Text Analysis and Structured Futuring to Create Story‐Based Scenarios

Abstract

The 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 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 10 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 original 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.

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“…Then, progressive activities focused on defining internal and external characteristics, motivations of what make the character act, obstacles that are relevant to their character, and thinking about the wants of a character versus what they might need for a satisfying story arc. In the second part of the workshop, participants designed a plot set in their world that their characters would navigate (Keys & Meyer, 2022). The exercises here were built around identifying ‘Story beats’, specifically: Act I Once upon a time… Every day… Until one day… Act II Because of this… Because of that… Act III Until finally… Ever since then… …”

“…NLTK), v3.4.4, and Gensim Python package v3.8.0. Following the methods of Keys and Meyer (2022), the LDA was performed sequentially including: pre-processing the documents (including tokenization, stopword removal, and lemmatization), sensitivity testing to identify the most suitable number of topics for the LDA, and visualization of the final text analysis. First, during tokenization each word across all documents in the corpus was converted into an individual unit (i.e.…”

“…Finally, if the keywords for a topic represented <1% of total terms across the entire corpus, then that topic was removed from the analysis. Topics with low term representation were not coherent enough for the story-based scenario process (Keys & Meyer, 2022).…”