Knowledge Diversity and User Innovation in Online Communities: A Machine Learning-Enabled Analysis

Kai Hartmayer and Diana Cervera

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

Abstract: Distributed user innovation increasingly unfolds in online communities, yet little is known about how community-level knowledge structures relate to its outcomes. Prior research emphasizes individual innovators or single communities, leaving cross-community differences underexplored. We draw on a corpus of 157 million Reddit posts and implement a three-stage pipeline: First, a keyword-based semantic filter identifies problem-solving content. Second, supervised classification detects user-generated solutions and their novelty. Finally, on a stratified sample of 150,000 posts across 15 communities, we measure embedding dispersion and use embedding-based topic modeling to derive knowledge diversity components. Results indicate that different dimensions of knowledge diversity are associated with distinct and potentially non-monotonic patterns of solution emergence and novelty, while community evaluation appears to favor solution presence over novelty. These findings suggest that knowledge diversity may relate to the generation of distributed innovation and demonstrate a scalable approach to detecting distributed user innovation from digital trace data.

Date: 2026-12
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