Sensemaking and lens-shaping: Identifying citizen contributions to foresight through comparative topic modelling

•This research uses natural language processing (Topic Modelling) to identifying novel contributions from participatory foresight workshops.•Computational textual analysis offers insights emerging from comparisons of expert and citizen based foresight products.•Natural Language Processing (NLP) tech...

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Bibliographic Details
Published in:Futures : the journal of policy, planning and futures studies planning and futures studies, 2021-05, Vol.129, p.102733, Article 102733
Main Authors: Rosa, Aaron B., Gudowsky, Niklas, Repo, Petteri
Format: Article
Language:English
Subjects:
Algorithms
Comparative analysis
Comparative foresight
Computational linguistics
Corporate culture
Language processing
Natural language interfaces
Natural language processing
Participation
Topic modelling
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Summary:•This research uses natural language processing (Topic Modelling) to identifying novel contributions from participatory foresight workshops.•Computational textual analysis offers insights emerging from comparisons of expert and citizen based foresight products.•Natural Language Processing (NLP) technologies offer new modes of examining foresight texts for sensemaking purposes in organizations.•Computational textual analysis can reshape organizational factors affecting sensemaking through citizen sourced foresight inputs. As foresight activities continue to increase across multiple arenas and types of organizations, the need to develop effective modes of reviewing future-oriented information against long-term goals and policies becomes more pressing. The activities of institutional sensemaking are vital in constructing potential and desired futures, but remain sensitive to organizational culture and ethos, thus raising concerns about whose futures are being constructed. In viewing foresight studies as a critical component in such sensemaking, this research investigates a method of textual analysis that deploys natural language processing algorithms (NLP). In this research, we introduce and apply the methodology of topic modelling for conducting a comparative analysis to explore how citizen-derived foresight differs from other institutional foresight. Finally we present prospects for further employing NLP for strategic foresight and futures studies.
ISSN:0016-3287
1873-6378
DOI:10.1016/j.futures.2021.102733