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Information 2017, 8(3), 73; doi:10.3390/info8030073

An Overview on Evaluating and Predicting Scholarly Article Impact

1
School of Software, Dalian University of Technology, Dalian 116620, China
2
Computing Center, Anshan Normal University, Anshan 114007, China
3
Library, Anshan Normal University, Anshan 114007, China
4
School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: David Bawden
Received: 23 May 2017 / Revised: 22 June 2017 / Accepted: 23 June 2017 / Published: 25 June 2017
View Full-Text   |   Download PDF [1803 KB, uploaded 26 June 2017]   |  

Abstract

Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars. View Full-Text
Keywords: scholarly big data; article impact; machine learning; data mining scholarly big data; article impact; machine learning; data mining
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Bai, X.; Liu, H.; Zhang, F.; Ning, Z.; Kong, X.; Lee, I.; Xia, F. An Overview on Evaluating and Predicting Scholarly Article Impact. Information 2017, 8, 73.

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