1.2 Computational text analysis complements existing methods
It is a staggering challenge to collect, interpret, and prioritize the vast literature on Arctic system change - and then synthesize this information into scenarios about the future. Systematic meta-analysis of Arctic futures work (Arbo et al., 2013) has revealed the utility of synthetic review, but such meta-analysis may also be too narrowly focused on relatively small sample sizes (e.g., less than 100 articles). Others have argued that there is simply too much content to consume, and, as a result, pre-existing knowledge and biases are employed to filter content (Schatzmann et al., 2013). Thus, there is a need for complimentary approaches that can scan the information across hundreds or thousands of documents and across a broad range of disciplines to accurately understand the trajectory of where the world is heading (Kwon et al., 2017).
In thinking about the future Arctic, it is necessary to think well outside-the-box regarding nonlinear changes in technology and global change (Dator, 1993, 2019; Johnson, 2011). One method for addressing biased selection of evidence can be to develop a large corpus of literature that captures a very broad range of content related to the future Arctic. However, human cognitive biases actively disregard information that seems outlandish or strange (Schoemaker, 2004), making unbiased interpretation a persistent challenge.
Computational text analysis can assist in addressing this bias by providing a complimentary means of thematic identification and analysis of a corpus of literature. In other words, a computer program can be used to potentially reduce human selection bias by ‘reading’ the thousands of texts and ‘interpreting’ different patterns than a human might see. Specifically, machine learning-based latent Dirichlet allocation can enable the rapid analysis of large, text-based datasets to reveal thematically distinct clusters of information (Asmussen & Møller, 2019).
This approach has been used in a limited way for scenarios related to drone technology (Kwon et al., 2017) and electric cars (Kim et al., 2016). Considerable opportunity exists to leverage these methods and fully incorporate them into detailed visions of the future Arctic (Kayser & Blind, 2017; Kayser & Shala, 2016).