Bibliometric impact measures leveraging topic analysis

Measurements of the impact and history of research literature provide a useful complement to scientific digital library collections. Bibliometric indicators have been extensively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in latent topic models have produced promising results for automatic sub-field discovery. The fine-grained, faceted topics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system. Categories and Subject Descriptors H.3.7 [Information Storage and Retrieval]: D...

• Type: Conference
• Year: 2006
• Where: JCDL
• Authors: Gideon S. Mann, David M. Mimno, Andrew McCallum
• Keywords: JCDL 2006, Latent Topic Models, Research Literature, Topic Models