Paper: Predicting a Scientific Community's Response to an Article

ACL ID D11-1055
Title Predicting a Scientific Community's Response to an Article
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We consider the problem of predicting mea- surable responses to scientific articles based primarily on their text content. Specif- ically, we consider papers in two fields (economics and computational linguistics) and make predictions about downloads and within-community citations. Our approach is based on generalized linear models, allowing interpretability; a novel extension that cap- tures first-order temporal effects is also pre- sented. We demonstrate that text features significantly improve accuracy of predictions over metadata features like authors, topical categories, and publication venues.