Identifying Key Users and Topics from Online Learning Community
Cai Yongming1(),Liu Lu1,Wang Kewei2
1Business School, University of Jinan, Jinan 250002, China 2School of Economics and Management, Inner Mongolia University of Technology, Huhhot 010051, China
[Objective] This study automatically analyzes resources of a virtual learning community, aiming to address the issue of information overload. [Methods] We proposed a hyper-network LDA model based on the user-document-word cube. Then, we modified this LDA model with the help of word and user analysis. Finally, we improved the cohesiveness of topics in the hyper-network LDA model, through increasing the distribution probability of closely connected words or users for the same topics. [Results] Compared to the traditional social network analysis methods, the proposed LDA model can identify important users, key topics and the relationship among them, as well as user preferences with frequency matrix of user-vocabulary and distribution probability of user-topic. [Limitations] Hyper-network analysis theory is still developing and we only studied the weighted un-directed network, which does not include the relationship of posting and replying. [Conclusions] The hyper-network LDA model effectively analyzes topics of short texts and online interactions, which are of significance to users and online learning community managers.
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003,3:993-1022.
[2]
Rosen-Zvi M, Griffiths T, Steyvers M, et al. The Author-Topic Model for Authors and Documents [C]//Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 2004: 487-494.
[3]
Tobarra L, Robles-Gómez A, Ros S, et al. Analyzing the Students’ Behavior and Relevant Topics in Virtual Learning Communities[J]. Computers in Human Behavior, 2014,31:659-669.
doi: 10.1016/j.chb.2013.10.001
[4]
Jenders M, Krestel R, Naumann F. Which Answer is Best?: Predicting Accepted Answers in Mooc Forums [C]//Proceedings of the International Conference Companion on World Wide Web. 2016: 679-684.
( Sun Chuanyuan, Liu Yumei. The Evaluation of University Video Open Courses of China——Content Analysis of “Wonderful Comments” Based on I-Courses Web[J]. Modern Educational Technology, 2013,23(12):91-95.)
( Lu Lu, Ding Caichang. Model of Identifying the Influentials in Blog Community[J]. Computer Science, 2011,38(S1):165-168.)
[7]
Li D F, Ding Y, Sugimoto C, et al. Modeling Topic and Community Structure in Social Tagging: The TTR-LDA-Community Model[J]. Journal of the Association for Information Science Technology, 2011,62(9):1849-1866.
( Liao Xiao, Li Zhihong, Xi Yunjiang. Knowledge Discovery Methods on User-Experts in Enterprise Virtual Communities Based on Weighted Knowledge Network[J]. Systems Engineering-Theory & Practice, 2016,36(5):1268-1279.)
[9]
Deng Q, Wang Z. Degree Centrality in Scientific Collaboration Supernetwork[C]//Proceedings of the International Conference on Information Science & Technology, Nanjing, China. 2011: 259-262.
( Guo Qiuping, Liang Mengli, Liu Xiuli, et al. Research on Knowledge Correlation in Hypernetwork Based on Author-Keyword-Citation Multiple Co-occurrence[J]. Information Studies: Theory & Application, 2016,39(7):20-26.)
[11]
Zhao L M, Zhang H H, Wu W Q. Cooperative Knowledge Creation in an Uncertain Network Environment Based on a Dynamic Knowledge Supernetwork[J]. Scientometrics, 2019,119(2):657-685.
doi: 10.1007/s11192-019-03049-4
( Cai Yongming, Chang Qing. Chinese Short Text Topic Analysis by Latent Dirichlet Allocation Model with Co-word Network Analysis (CA-LDA)[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(3):305-317.)
[13]
Denning P. The Science of Computing: Supernetworks[J]. American Scientist, 1985,73(3):225-227.
[14]
Kleinberg J M. Authoritative Sources in a Hyperlinked Environment[J]. Journal of the ACM, 1999,46(5):604-632.
doi: 10.1145/324133.324140
[15]
Birkbak A, Carlsen H B. The World of Edgerank: Rhetorical Justifications of Facebook’s News Feed Algorithm [J]. Computational Culture, 2016(5):1-24.
[16]
Bródka P, Skibicki K, Kazienko P, et al. A Degree Centrality in Multi-Layered Social Network [C]//Proceedings of the International Conference on Computational Aspects of Social Networks. 2011: 19-21.
[17]
Spatocco C, Stilo G, Domeniconi C, et al. A New Framework for Centrality Measures in Multiplex Networks [OL]. arXiv Preprint, arXiv: 1801. 08026.
[18]
Girvan M, Newman M E. Community Structure in Social and Biological Networks[J]. Proceedings of the National Academy of Sciences, 2002,99(12):7821-7826.
doi: 10.1073/pnas.122653799
[19]
Newman M E J. Detecting Community Structure in Networks[J]. European Physical Journal B, 2004,38:321-330.
doi: 10.1140/epjb/e2004-00124-y
[20]
Bródka P, Filipowski T, Kazienko P. An Introduction to Community Detection in Multi-Layered Social Network [A]// Lytras M D, Ruan D, Tennyson R D, et al. Information Systems,E-Learning, and Knowledge Management Research[M]. Springer Berlin Heidelberg, 2013: 185-190.
[21]
Wilson J D, Palowitch J, Bhamidi S, et al. Community Extraction in Multilayer Networks with Heterogeneous Community Structure[J]. Journal of Machine Learning Research, 2017,18(1):5458-5506.
[22]
Amelio A, Mangioni G, Tagarelli A. Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling[J]. IEEE Transactions on Network Science and Engineering. DOI: 10.1109/TNSE.2019.2913325.
doi: 10.1109/TNSE.2016.2537545
pmid: 28435844
[23]
Jordan M. Learning in Graphical Models[M]. Netherlands: Springer, 1998.
[24]
Griffiths T L, Steyvers M. Finding Scientific Topics[J]. Proceedings of the National Academy of Sciences, 2004,101(1):5228-5235.
doi: 10.1073/pnas.0307752101