Identifying Topics and Trends in the Study of Common-Pool Resources Using Natural Language Processing

The rapid growth of the literature on the commons poses an immense challenge for the synthesis and advancement of knowledge. While it may have been reasonable for previous generations of scholars to keep up to date with a literature adding thirty to fifty papers each year, there are now hundreds of papers on the commons published each year in addition to those that might be relevant to researchers on the basis of particular sectors, methods, disciplines or theories. This paper exploits recent advances in natural language processing to identify topics and trends in the literature on the commons over the past thirty years using a dynamic topic model. The results highlight the centrality of key themes concerning resources, property rights and local management, alongside growing interest in the topics of conservation and local management. The results also demonstrate the diversity of the field with topics ranging from forests, fisheries and land to urban areas and software. Overall the dynamic topic model appears to provide a useful approach for synthesizing high-level features of the literature.