Domain-topic models with chained dimensions: charting the evolution of a major oncology conference (1995-2017)
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This paper presents three main contributions to the computational study of science from bibliographic corpora. First, by combining hypergraphs and stochastic block models, it introduces a new approach to model corpora based on their substantive contents and integrating both temporal and other metadata dimensions. We call this simultaneous modeling of documents and words "domain-topic models", and their integration with metadata their "chained dimensions". Second, the paper introduces a new form of interactive map for the exploration of hypergraph data that enables the seamless navigation of the different dimensions, scales, and their relations, as expressed in the models, and describes the steps to accurately read these new science maps. Third, it introduces a new corpus that is both of great interest to current STS research and an exemplary case for the new methodology presented here: the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference. It is shown that the new approach, named SASHIMI, is able to infer thematic clusters in the corpus, describe them as assemblages of topics, and detect the presence of significant temporal patterns, identifying the major thematic transformations of oncology during the period.
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