Analyzing & Clustering Enterprise Microblog Users with Supernetwork
Xi Yunjiang1,Du Diedie1,Liao Xiao2(),Zhang Xuehong1
1School of Business Administration, South China University of Technology, Guangzhou 510641, China 2School of Internet Finance and Information Engineering, Guangdong University of Finance,Guangzhou 510521, China
[Objective] This paper proposes an integrated modeling method to process multi-dimensional user interest data, aiming to examine the spectral clustering method for analyzing user interests. [Methods] First, we retrieved Weibo (Microblog) data of "Three Squirrels" and used supernetwork model to integrate the modeling of contents and user interaction data. Then, we constructed an interactive interest index and grouped the users with spectral clustering algorithm. Finally, we evaluated the clustering results with the Silhouette Coefficient and Davies-Bouldin methods. [Results] We found that the clustering DB value reached 0.57 (k was set at 15), which was evenly distributed. [Limitations] More research is needed to further explore user characteristic data and the impacts of different data dimensions on user interests. [Conclusions] This study proposes maintenance and marketing suggestions for enterprise Weibo profiles, which will help them identify user interests and improve marketing effectiveness.
Dao W V T, Angelina N H L, Cheng J M S, et al. Social Media Advertising Value: The Case of Transitional Economies in Southeast Asia[J]. International Journal of Advertising, 2014,33(2):271-294.
[2]
Mago N, Shirwaikar R D, Acharya U D, et al. Partition and Hierarchical Based Clustering Techniques for Analysis of Neonatal Data[C]// Proceedings of International Conference on Cognition and Recognition. 2017: 345-355.
[3]
Zhang S C, Yu J. A New Connectivity-based Cluster Validity Index[C]// Proceedings of 2010 Chinese Conference on Pattern Recognition (CCPR). 2010.
[4]
Yamaguchi Y, Amagasa T, Kitagawa H. Tag-based User Topic Discovery Using Twitter Lists[C]// Proceedings of 2011 International Conference on Advances in Social Networks Analysis and Mining. 2011: 13-20.
[5]
Wu W, Zhang B, Ostendorf M. Automatic Generation of Personalized Annotation Tags for Twitter Users[C]// Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association of Computational Linguistics. 2010: 689-692.
( Wang Yanru, Ma Huifang, Liu Haijiao, et al. A Microblog User Interest Modeling Method Based on Multi-tag Semantic Correlation[J]. Computer Engineering & Science, 2018,40(11):2067-2073.)
( Xiong Huixiang, Ye Jiaxin. A Double-level Microblogs User Similarity Algorithm[J]. Journal of Intelligence, 2018,37(6):160-166.)
[8]
Wallner G, Kriglstein S, Drachen A. Tweeting Your Destiny: Profiling Users in the Twitter Landscape around an Online Game[OL]. arXiv Preprint, arXiv: 1905.12694.
( Li Pengfei, Dong Xu, Zhong Zhaoman, et al. Similar User Mining Based on User Interest Topics in Weibo[J]. Computer Engineering and Applications, 2019,55(11):102-109.)
[10]
Sohail A, Cheema M A, Taniar D. Geo-social Temporal Top-k Queries in Location-based Social Networks[A]//Databases Theory and Applications[M]. Springer, 2020: 147-160.
[11]
Wan L, Hong Y M, Huang Z, et al. A Hybrid Ensemble Learning Method for Tourist Route Recommendations Based on Geo-tagged Social Networks[J]. International Journal of Geographical Information Science, 2018,32(11):2225-2246.
( Yu Diqian. Micro-blog User Behavior Analyzing and Forecasting Method:China,CN201711078084.0[P]. 2018-04-13. [2018-04-13].
[13]
Ma H F, Jia M H Z, Zhang D, et al. Combining Tag Correlation and User Social Relation for Microblog Recommendation[J]. Information Sciences, 2017,385(C):325-337.
[14]
万子玮. 基于主题词的微博用户兴趣模型研究[D]. 北京:首都经济贸易大学, 2018.
[14]
( Wan Ziwei. Research on Weibo User Interest Model Based on Topic Words[D]. Beijing: Capital University of Economics and Business, 2018.)
[15]
Sheffi Y. Urban Transportation Networks: Equi-librium Analysis with Mathematical Programming Methods[M]. Printice-Hall, 1985.
[16]
Nagurney A, Cruz J, Dong J, et al. Supply Chain Networks, Electronic Commerce, and Supply Side and Demand Side Risk[J]. European Journal of Operational Research, 2005,164(1):120-142.
( Wang Shoubiao, Li Xinming, Liu Dong. Super-network Model of Architecture for Weapon Equipment System of Systems Based on Granular Computing[J]. Journal of Systems Engineering and Electronics, 2016,38(4):836-843.)
( Hu Miheng. Modeling of Entity Relationship Network in the Internet of Things Based on Hypergraph Theory[J]. Computer Knowledge and Technology, 2018,14(5):41-43.)
[19]
Shang Y C, Wang H S, Wang Y L. The Supernetwork Model of Social Networking Services[J]. Journal of Donghua University(English Edition), 2012,29(1):37-39.
[20]
Lian Y, Dong X F, Chi Y X, et al. An Internet Water Army Detection Supernetwork Model[J]. IEEE Access, 2019,7:55108-55120.
[21]
Chi Y X, Tang X Y, Lian Y, et al. A Supernetwork-based Online Post Informative Quality Evaluation Model[J]. Knowledge-based Systems, 2019,168:10-24.
( Wang Dan, Zhang Haitao, Liu Yashu, et al. Sentiment Analysis and Ideological Guidance of Key Nodes in Micro-blog Public Opinion[J]. Library and Information Service, 2019,63(4):15-22.)
( Ji Yixiao, Wu Chensi, Yang Su, et al. Network Security Event Chain Evolution Model Based on Super Network[J]. Journal of Cyber Security, 2019,4(1):89-100.)
[24]
Nguyen M D, Shin W Y. DBSTexC: Density-based Spatio-textual Clustering on Twitter[C]// Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2017: 23-26.
( Zheng Jiehui. Implementation of Microblog User Interest Discovery Based on Clustering Mining Algorithm[J]. Network Security Technology & Application, 2017(10):48-49, 56.)
[26]
Shi J B, Malik J. Normalized Cuts and Image Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(8) : 888-905.
[27]
徐洪元. 社会媒体群组探测的谱聚类研究与应用[D]. 武汉: 武汉理工大学, 2016.
[27]
( Xu Hongyuan. Spectral Clustering Research and Application on Community Detection of Social Media[D]. Wuhan: Wuhan University of Technology, 2016.)
[28]
Tran C, Kim J Y, Shin W Y, et al. Clustering-based Collaborative Filtering Using an Incentivized/Penalized User Model[J]. IEEE Access, 2019,7:62115-62125.
[29]
Zhang S X, Zhang S Y, Yen N Y, et al. The Recommendation System of Micro-blog Topic Based on User Clustering[J]. Mobile Networks and Applications, 2017,22(2):228-239.
( Xiong Huixiang, Jiang Wuxuan. Clustering and Recommending Users Based on Tags and Relation Network[J]. Data Analysis and Knowledge Discovery, 2017,1(6):36-46.)
( Liao Xiao, Ye Guangyu, Li Weichan, et al. The Methods to Mine Fans Interests of Enterprise Micro-blog Based on the Integration of Text and Behavior Data[J]. Systems Engineering, 2019,37(2):139-149.)
[32]
Von Luxburg U. A Tutorial on Spectral Clustering[J]. Statistics and Computing, 2007,17(4):395-416.
[33]
Kardaras D K, Kaperonis S, Barbounaki S, et al. An Approach to Modelling User Interests Using TF-IDF and Fuzzy Sets Qualitative Comparative Analysis[C]// Proceedings of IFIP International Conference on Artificial Intelligence Applications and Innovations. 2018: 606-615.
[34]
Wang W J, Xu Z B, Lu W Z, et al. Determination of the Spread Parameter in the Gaussian Kernel for Classification and Regression[J]. Neurocomputing, 2003,55(3/4):643-663.
doi: 10.1016/S0925-2312(02)00632-X
( An Xingru. The Research on the Threshold of High-frequency Words Based on the Normal Distribution in Word Frequency Analysis[J]. Journal of Intelligence, 2014,33(10):129-136.)