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Detecting Research Fronts and Potentially Valuable Papers Using Paper Metadata

Authors :
Jingsong Yu
Bowen Zhao
Guanlin Li
Source :
WI/IAT
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

Conventional research trend detection methods require a large number of papers to perform analysis, while the trends detected are usually retrospective and cannot represent the emerging topic which is in embryonic stage, because detection method based on word features or citation relationships have to wait for the trend to come into form and become popular to detect it. Besides, the acquisition of paper full text is usually costly. We employ the paper metadata, which is relatively easy to acquire and process, to detect potentially valuable papers in specific field once the papers are published or their metadata become accessible and extract emerging cutting-edge trend from them. By comparing the result with conventional methods, we prove the effectiveness of the method.

Details

Database :
OpenAIRE
Journal :
2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
Accession number :
edsair.doi...........725106d0df36183e16239ae9b1c1a731