Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2017

Abstract

Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction model are valid for a topic-based approach. Our results indicate the customized model is able to predict citations for many of the topics considered in our study with reasonably high accuracy. Insights from these results indicate the promise of citation of prediction of research topics, and its utility for individual researchers, as well as research groups.

Keywords

Citation prediction, Software engineering publication, Topic model

Discipline

Numerical Analysis and Scientific Computing | Scholarly Publishing | Software Engineering

Research Areas

Information Systems and Management

Publication

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion, April 3-7, Perth, Australia

First Page

1251

Last Page

1257

ISBN

9781450349147

Identifier

10.1145/3041021.3053051

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3041021.3053051

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