Predicting the impact of software engineering topics: An empirical study
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 ecosy...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2017
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5586 https://ink.library.smu.edu.sg/context/sis_research/article/6589/viewcontent/Predict_Impact_SoftwareEng_2017_pv.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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