| Autor: |
Jiaxin Tang, Yang Chen, Guozhen She, Yang Xu, Kewei Sha, Xin Wang, Yi Wang, Zhenhua Zhang, Pan Hui |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 11, Iss 15, p 6912 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app11156912 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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