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 . Written communication is an essential component of the complex social phenomenon of science . As such , natural language processing is well-positioned to provide tools for understanding the scientific process , by analyzing the textual artifacts ( papers , proceedings , etc . ) that it produces . This paper is about modeling collections of scientific documents to understand how their textual content relates to how a scientific community responds to them . While past work has often focused on citation structure ( Borner et al . , 2003 ; Qazvinian and Radev , 2008 ) , our emphasis is on the text content , following Ramage et al . ( 2010 ) and Gerrish and Blei ( 2010 ) . Instead of task-independent exploratory data analysis ( e.g. , topic modeling ) or multi-document sum-marization , we consider supervised models of the collective response of a scientific community to a published article . There are many measures of impact of a scientific paper ; ours come from direct measurements of the number of downloads ( from an established website where prominent economists post papers before formal publication ) and citations ( within a fixed scientific community ) . We adopt a discriminative approach based on generalized linear models that can make use of any text or metadata features , and show that simple lexical features offer substantial power in modeling out-ofsample response and in forecasting response for future articles . Realistic forecasting evaluations require methodological care beyond the usual best practices of train / test separation , and we elucidate these issues . In addition , we introduce a new regularization technique that leverages the intuition that the relationship between observable features and response should evolve smoothly over time . This regularizer allows the learner to rely more strongly on more recent evidence , while taking into account a long history of training data . Our time series-inspired regularizer is computationally efficient in learning and is a significant advance over earlier text-driven forecasting models that ignore the time variable altogether ( Kogan et al . , 2009 ; Joshi et al . , 2010 ) . We evaluate our approaches in two novel experimental settings : predicting downloads of economics articles and predicting citation of papers at ACL conferences . Our approaches substantially outper-594 form text-ignorant baselines on ground-truth predictions . Our time series models permit flexibility in features and offer a novel and perhaps more interpretable view of the data than summary statistics . We presented a statistical approach to predicting a scientific community 's response to an article , based on its textual content . To improve the interpretability of the linear model , we developed a novel time series regularizer that encourages gradual changes across time steps . Our experiments showed that text features significantly improve accuracy of predictions over baseline models , and we found that the feature weights learned with the time series regularizer reflect important trends in the literature .

Challenge: Textual information is ignored for paper citation or download count prediction.
Approach: They propose to use textual features in scientific articles for supervised generalized linear models coupled with a new regularization technique to predict the community's response.
Outcome: Using textual features significantly improves on citation count prediction task of economics and NLP papers over baseline models that just use metadata such as authors.