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