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Facebook Customer Engagement Graph Analysis Using K-core, M-core and Km-core Methods

Published: 06 August 2021 Publication History

Abstract

Customer engagement in Facebook fan page of a brand can be rationalized as a network from customer reactions towards the moderator postings. In this paper the network of consumers connected by the posts of two supermarket chains are represented in different forms of graphs. Here a graph analytic framework, which adopts the concept of Social Network Analysis to examine the structure of the graphs, is presented. The graph filtering methods, k-core, m-core or m-slice and km-core, a combination of the former cores, are utilized to examine the customer engagement behavior, to identify and to filter the consumer communities. For both supermarket brands, most of the customer attitudes toward the advertising and promotion posts are positive. Their customer engagement behaviors are similar, in that the majority of customers are engaged by a single post advertising a discount promotion, greater than 90%, following power-laws with respect to the threshold of consumer degree and co-reaction posts. There are around 3% of customers consuming both brands.

References

[1]
Gebert, H., Geib, M., Kolbe, L., and Riempp, G. 2002. Towards customer knowledge management: Integrating customer relationship management and knowledge management concepts. In Proceedings of the Second International Conference on Electronic Business (ICEB 2002). pp. 296-298.
[2]
Manthiou, A., Chiang, L., and Tang, L. R. 2013. Identifying and responding to customer needs on Facebook fan pages. International Journal of Technology and Human Interaction (IJTHI), 9(3), 36-52.
[3]
Cvijikj, I. P., and Michahelles, F. 2013. Online engagement factors on Facebook brand pages. Social network analysis and mining, 3(4), 843-861.
[4]
Coulter, K. S., Gummerus, J., Liljander, V., Weman, E., and Pihlström, M. 2012. Customer engagement in a Facebook brand community. Management Research Review.
[5]
Ruiz-Mafe, C., Marti-Parreno, J., and Sanz-Blas, S. 2014. Key drivers of customer loyalty to Facebook fan pages. Online Information Review.
[6]
Kang, J., Tang, L., and Fiore, A. M. 2014. Enhancing customer–brand relationships on restaurant Facebook fan pages: Maximizing customer benefits and increasing active participation. International Journal of Hospitality Management, 36, 145-155.
[7]
Nugroho, A., and Agustina, A. 2020. Examining Corporate Engagement in Social Media: Advancing The Use of Facebook for Corporation Page.  CoverAge: Journal of Strategic Communication, 10(2), 1-10.
[8]
Krebs, F., Lubascher, B., Moers, T., Schaap, P., and Spanakis, G. 2017. Social emotion mining techniques for Facebook posts reaction prediction. arXiv preprint arXiv:1712.03249.
[9]
Barabási, A. L., and Bonabeau, E. 2003. Scale-free networks. Scientific american, 288(5), 60-69.
[10]
Klaysri, T. 2019. Analysis of category co-occurrence in Wikipedia networks. Doctoral dissertation, Birkbeck, University of London.
[11]
Isaak, J., and Mina J. H. 2018. User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer 51.8: 56-59.
[12]
Newman, M. E. 2001. The structure of scientific collaboration networks. Proceedings of the national academy of sciences, 98(2), 404-409.
[13]
Al-Taie, M., and Kadry, S. 2012. Applying social network analysis to analyze a web-based community. arXiv preprint arXiv:1212.6050.
[14]
Tang, T., Hämäläinen, M., Virolainen, A., and Makkonen, J. 2011. Understanding user behavior in a local social media platform by social network analysis. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. pp. 183-188.
[15]
Hong, S. H., and Lu, S. 2020. Graph sampling methods for big complex networks integrating centrality, k-core, and spectral sparsification. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. pp. 1843-1851.
[16]
Ruan, M., Ren, X., Li, G., Ogunbona, P. O., and Wu, J. 2018, September. K-Core Graph-Based Retinal Vascular Registration. In Proceedings of the 2nd International Conference on Biomedical Engineering and Bioinformatics. pp. 70-73.
[17]
Garcia, D., Mavrodiev, P., and Schweitzer, F. 2013. Social resilience in online communities: The autopsy of friendster. In Proceedings of the first ACM conference on Online social networks. pp. 39-50.
[18]
Kamakura, N., Takahashi, H., Nakamura, K., and Kanaya, S. 2010. Protein function prediction based on k-cores of interaction networks. In 2010 International Conference on Bioinformatics and Biomedical Technology. pp. 211-215. IEEE.
[19]
Eidsaa, M., and Almaas, E. 2013. S-core network decom-position: A generalization of k-core analysis to weighted networks. Physical Review E, 88(6), 062819.
[20]
Cheng, Y., Lu, C., and Wang, N. 2013. Local k-core clustering for gene networks. In 2013 IEEE International Conference on Bioinformatics and Biomedicine. pp. 9-15.
[21]
Giatsidis, C., Thilikos, D. M., and Vazirgiannis, M. 2011. Evaluating cooperation in communities with the k-core structure. In 2011 International conference on advances in social networks analysis and mining. pp. 87-93. IEEE.
[22]
Chin, A., and Chignell, M. 2006. A social hypertext model for finding community in blogs. In Proceedings of the seventeenth conference on Hypertext and hypermedia. pp. 11-22.
[23]
Holloway, T., Bozicevic, M., and Börner, K. 2007. Analyzing and visualizing the semantic coverage of Wikipedia and its authors. Complexity, 12(3), 30-40.
[24]
Klaysri, T., Fenner, T., Lachish, O., Levene, M., and Papapetrou, P. 2013. Analysis of Cluster Structure in Large-scale English Wikipedia Category Networks. In proceedings of the International Symposium on Intelligent Data Analysis. Springer. pp. 261-272.
[25]
Özgür, A., Cetin, B., and Bingol, H. 2008. Co-occurrence network of reuters news. International Journal of Modern Physics C, 19(05), 689-702.
[26]
Leifeld, P., and Haunss, S. 2012. Political discourse networks and the conflict over software patents in Europe. European Journal of Political Research, 51(3), 382-409.
[27]
Yu, Q., Shao, H., and Duan, Z. 2011. Research groups of oncology co-authorship network in China. Scientometrics, 89(2), 553-567.
[28]
Newman, M. E. 2005. Power laws, Pareto distributions and Zipf's law. Contemporary physics, 46(5), 323-351.
[29]
Adamic, L. A. 2000. Zipf, power-laws, and pareto-a ranking tutorial. Xerox Palo Alto Research Center, Palo Alto, CA.
[30]
Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47.

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ICCDE '21: Proceedings of the 2021 7th International Conference on Computing and Data Engineering
January 2021
110 pages
ISBN:9781450388450
DOI:10.1145/3456172
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Published: 06 August 2021

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Author Tags

  1. Facebook customer engagement. Graph Analysis
  2. Social Network Analysis
  3. k-core
  4. m-core

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