Exploring Machine-based Idea Landscapes – The Impact of Granularity

Wahl, Julian
Stroehle, Thomas
Füller, Johann
Hutter, Katja
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Effective exploration of a landscape full of crowdsourced ideas depends on the right search strategy, as well as the level of granularity in the representation. To categorize similar ideas on different granularity levels modern natural language processing methods and clustering algorithms can be usefully applied. However, the value of machine-based categorizations is dependent on their comprehensibility and coherence with human similarity perceptions. We find that machine-based and human similarity allocations are more likely to converge when comparing ideas across more distant solution clusters than within closely related ones. Our exploratory study contributes to research on the navigability of idea landscapes, by pointing out the impact of granularity on the exploration of crowdsourced knowledge. For practitioners, we provide insights on how to organize the search for the best possible solutions and control the cognitive demand of searchers.
Collaboration in Online Communities: Information Processing and Decision Making, crowdsourcing, document embeddings, granularity, idea exploration, similarity
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