2006 BibliometricImpactMeasuresLever

• (Mann et al., 2006) ⇒ Gideon S. Mann, David Mimno, and Andrew McCallum. (2006). “Bibliometric Impact Measures Leveraging Topic Analysis.” In: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries. doi:10.1145/1141753.1141765

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Bibliometric+impact+measures+leveraging+topic+analysis%22+2006
• http://dl.acm.org/citation.cfm?doid=1141753.1141765&preflayout=flat#citedby

Quotes

Abstract

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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 BibliometricImpactMeasuresLever	Gideon S. Mann David Mimno			Bibliometric Impact Measures Leveraging Topic Analysis				10.1145/1141753.1141765