A Language-based Approach to Measuring Scholarly Impact

[Jason Priem]

Interesting work, with nice results.

Gerrish, S. M., EDU, P., & Blei, D. M. (2010). A Language-based Approach
to Measuring Scholarly Impact. Presented at the 27th International
Conference on Machine Learning (ICML 2010), Haifa, Israel.

Abstract: Identifying the most influential documents in a corpus is an
important problem in many fields, from information science and
historiography to text summarization and news aggregation.
Unfortunately, traditional bibliometrics such as citations are often not
available. We propose using changes in the thematic content of documents
over time to measure the importance of individual documents within the
collection. We describe a dynamic topic model for both quantifying and
qualifying the impact of these documents. We validate the model by
analyzing three large corpora of scientific articles. Our measurement of
a document's impact correlates significantly with its number of citations.