Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning

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http://hdl.handle.net/10138/333297

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Tang , J , Chen , Y , She , G , Xu , Y , Sha , K , Wang , X , Wang , Y , Zhang , Z & Hui , P 2021 , ' Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning ' , Applied sciences (Basel) , vol. 11 , no. 15 , 6912 . https://doi.org/10.3390/app11156912

Title: Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
Author: Tang, Jiaxin; Chen, Yang; She, Guozhen; Xu, Yang; Sha, Kewei; Wang, Xin; Wang, Yi; Zhang, Zhenhua; Hui, Pan
Contributor organization: Helsinki Institute of Life Science HiLIFE
Department of Mathematics and Statistics
Department of Computer Science
Date: 2021-08
Language: eng
Number of pages: 22
Belongs to series: Applied sciences (Basel)
ISSN: 2076-3417
DOI: https://doi.org/10.3390/app11156912
URI: http://hdl.handle.net/10138/333297
Abstract: Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar's author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.
Subject: Google Scholar
author profiles
mis-configuration
machine learning
neural network
node embedding
NETWORKS
DEFENSE
INDEX
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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