Date of Original Version

7-2011

Type

Conference Proceeding

Journal Title

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

First Page

594

Last Page

604

Rights Management

Copyright 2011 ACL

Abstract or Description

We consider the problem of predicting measurable responses to scientific articles based primarily on their text content. Specifically, 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 captures first-order temporal effects is also presented. We demonstrate that text features significantly improve accuracy of predictions over metadata features like authors, topical categories, and publication venues.

Creative Commons License

Creative Commons Attribution-Noncommercial-Share Alike 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

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Published In

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 594-604.