Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Expert finding and Q&A systems · Advanced Graph Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, Fudan University, Shanghai 200433, China
Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China
Department of Computing Sciences, University of Houston-Clear Lake, Houston, TX 77058, USA
Peng Cheng Laboratory, Shenzhen 518055, China
Institute of Future Networks, Southern University of Science and Technology, Shenzhen 518055, China
Meituan, Beijing 100102, China
Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Funding
National Natural Science Foundation of China
References (61)
Show source-provided references
1. Wikiometrics: A Wikipedia based ranking system10.1007/s11280-016-0427-8 ↗
2. Citation analysis with microsoft academic10.1007/s11192-017-2247-8 ↗
3. Mining a Web Citation Database for author co-citation analysis10.1016/S0306-4573(01)00046-2 ↗
4. Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases10.1007/s11192-018-2958-5 ↗
5. Chen, Y., Ding, C., Hu, J., Chen, R., Hui, P., and Fu, X. (2017, January 3–7). Building and Analyzing a Global Co-Authorship Network Using Google Scholar Data. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.10.1145/3041021.3053056 ↗
6. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses10.1096/fj.07-9492LSF ↗
7. Citation analysis: Comparison of Web of Science®, Scopus™, SciFinder®, and Google Scholar
8. Using the H-index of concentration with published data10.2307/1928815 ↗
9. Using Google Scholar institutional level data to evaluate the quality of university research10.1007/s11192-017-2532-6 ↗
10. Author Name Disambiguation10.1002/aris.2009.1440430113 ↗
11. Han, H., Giles, L., Zha, H., Li, C., and Tsioutsiouliklis, K. (2004, January 11). Two supervised learning approaches for name disambiguation in author citations. Proceedings of the 4th Joint ACM/IEEE Conference on Digital Libraries, Tucson, AZ, USA.10.1145/996350.996419 ↗
12. Author Name Disambiguation in MEDLINE10.1145/1552303.1552304 ↗
13. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z. (2008, January 24–27). ArnetMiner: Extraction and Mining of Academic Social Networks. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.10.1145/1401890.1402008 ↗
14. Citation index and impact factor10.4103/0378-6323.98093 ↗
15. (2021, June 01). Cloud Translation/Google Cloud. Available online: https://cloud.google.com/translate.
16. Latent Dirichlet Allocation
17. Learning the parts of objects by non-negative matrix factorization10.1038/44565 ↗
18. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis10.1016/0377-0427(87)90125-7 ↗
19. The Generalization of ’Student’s’ Problem when Several Different Population Variances are Involved
20. Information gain and a general measure of correlation10.1093/biomet/70.1.163 ↗
21. Kim, Y. (2014, January 25–29). Convolutional Neural Networks for Sentence Classification. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.10.3115/v1/D14-1181 ↗
22. Koutník, J., Greff, K., Gomez, F., and Schmidhuber, J. (2014, January 21–26). A Clockwork RNN. Proceedings of the 31st International Conference on Machine Learning, Beijing, China.
23. Grover, A., and Leskovec, J. (2016, January 13–17). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939754 ↗
24. CSTeller: Forecasting scientific collaboration sustainability based on extreme gradient boosting10.1007/s11280-019-00703-y ↗
25. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4–9). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
26. Li, M., Zhang, T., Chen, Y., and Smola, A.J. (2014, January 24–27). Efficient Mini-batch Training for Stochastic Optimization. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.10.1145/2623330.2623612 ↗
27. Dropout: A Simple Way to Prevent Neural Networks from Overfitting
28. An introduction to ROC analysis10.1016/j.patrec.2005.10.010 ↗
29. The Regression Analysis of Binary Sequences10.1111/j.2517-6161.1958.tb00292.x ↗
30. A survey of decision tree classifier methodology10.1109/21.97458 ↗
31. Random Forests10.1023/A:1010933404324 ↗
32. Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation10.1109/TNSE.2018.2813672 ↗
33. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12–17). Hierarchical attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.10.18653/v1/N16-1174 ↗
34. Note on the sampling error of the difference between correlated proportions or percentages10.1007/BF02295996 ↗
35. Learning to Forget: Continual Prediction with LSTM10.1162/089976600300015015 ↗
36. Chang, S., Zhang, Y., Han, W., Yu, M., Guo, X., Tan, W., Cui, X., Witbrock, M., Hasegawa-Johnson, M.A., and Huang, T.S. (2017, January 4–9). Dilated Recurrent Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
37. (2021, June 01). MaxMind GeoLite Databases. Available online: https://dev.maxmind.com/geoip/geoip2/geolite2/.
38. (2021, June 01). Computer Science & Information Systems/Top Universities. Available online: https://www.topuniversities.com/university-rankings/university-subject-rankings/2018/computer-science-information-systems.
39. (2021, June 01). World University Rankings 2019 by Subject: Computer Science/Times Higher Education (THE). Available online: https://www.timeshighereducation.com/world-university-rankings/2018/subject-ranking/computer-science.
40. Successive H-Indices10.1007/s11192-007-0112-x ↗
41. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.J.P., and Wang, K. (2015, January 18–22). An Overview of Microsoft Academic Service (MAS) and Applications. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.10.1145/2740908.2742839 ↗
42. Li, H., Councill, I., Lee, W.C., and Giles, C.L. (2006, January 23–26). CiteSeerx: An Architecture and Web Service Design for an Academic Document Search Engine. Proceedings of the 15th International Conference on World Wide Web, Scotland, UK.10.1145/1135777.1135926 ↗
43. Yoshida, M., Ikeda, M., Ono, S., Sato, I., and Nakagawa, H. (2010, January 19–23). Person Name Disambiguation by Bootstrapping. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland.10.1145/1835449.1835454 ↗
44. Khabsa, M., Treeratpituk, P., and Giles, C.L. (2015, January 21–25). Online Person Name Disambiguation with Constraints. Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, Knoxville, TN, USA.10.1145/2756406.2756915 ↗
45. Louppe, G., Al-Natsheh, H.T., Susik, M., and Maguire, E.J. (2016, January 21–23). Ethnicity Sensitive Author Disambiguation Using Semi-supervised Learning. Proceedings of the International Conference on Knowledge Engineering and the Semantic Web, Prague, Czech Republic.10.1007/978-3-319-45880-9_21 ↗
46. Fan, X., Wang, J., Lv, B., Zhou, L., and Hu, W. (2008, January 26–30). GHOST: An Effective Graph-based Framework for Name Distinction. Proceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley, CA, USA.10.1145/1458082.1458327 ↗
47. Zhang, B., and Al Hasan, M. (2017, January 6–10). Name Disambiguation in Anonymized Graphs Using Network Embedding. Proceedings of the 2017 ACM Conference on Information and Knowledge Management, Singapore.10.1145/3132847.3132873 ↗
48. A Unified Probabilistic Framework for Name Disambiguation in Digital Library10.1109/TKDE.2011.13 ↗
49. Zhang, Y., Zhang, F., Yao, P., and Tang, J. (2018, January 19–23). Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3219859 ↗
50. Yu, H., Kaminsky, M., Gibbons, P.B., and Flaxman, A. (2006, January 11–15). SybilGuard: Defending against Sybil Attacks via Social Networks. Proceedings of the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Pisa, Italy.10.1145/1159913.1159945 ↗
51. Yu, H., Gibbons, P.B., Kaminsky, M., and Xiao, F. (2008, January 21–23). Sybillimit: A Near-Optimal Social Network Defense against Sybil Attacks. Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, USA.10.1109/SP.2008.13 ↗
52. Shi, L., Yu, S., Lou, W., and Hou, Y.T. (August, January 30). Sybilshield: An agent-aided social network-based Sybil defense among multiple communities. Proceedings of the IEEE International Conference on Computer Communications, Nassau, Bahamas.
53. Danezis, G., and Mittal, P. (2009, January 8–11). SybilInfer: Detecting Sybil Nodes using Social Networks. Proceedings of the Network and Distributed System Security Symposium, Diego, CA, USA.
54. Cao, Q., Sirivianos, M., Yang, X., and Pregueiro, T. (2012, January 25–27). Aiding the Detection of Fake Accounts in Large Scale Social Online Services. Proceedings of the 9th Symposium on Networked Systems Design and Implementation, San Jose, CA, USA.
55. Sybilbelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection10.1109/TIFS.2014.2316975 ↗
56. Uncovering Social Network Sybils in the Wild10.1145/2556609 ↗
57. Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., and Ghosh, R. (2013, January 11–14). Spotting Opinion Spammers Using Behavioral Footprints. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.10.1145/2487575.2487580 ↗
58. DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks10.1109/MCOM.2018.1700575 ↗
59. He, X., Gong, Q., Chen, Y., Zhang, Y., Wang, X., and Fu, X. (2021). DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network. IEEE Trans. Dependable Secur. Comput.10.1109/TDSC.2021.3068307 ↗
60. Misra, S., Tayeen, A.S.M., and Xu, W. (2016, January 23–27). SybilExposer: An effective scheme to detect Sybil communities in online social networks. Proceedings of the IEEE International Conference on Communications, Kuala Lumpur, Malaysia.10.1109/ICC.2016.7511603 ↗
61. Semi-supervised learning10.1007/BF00114803 ↗