Criteria for evaluating the effectiveness of social media users - a framework based on social media exploration
Subject Areas : General
rojyar pirmohammadiani
1
()
shahriyar mohammadi
2
(K.N. Toosi University of Technology)
Keywords: Impact value, social media exploration, user interactive behaviors,
Abstract :
Nowadays, users' interactive behaviors on social media have become an important and influential resource on marketing activities in various businesses. Despite the importance of this issue, providing appropriate criteria for evaluating the influential behavior of users in recent studies has received less attention. For this purpose, in the first step, an innovative theory framework including two main dimensions: potential of the influence and the level of the influence is presented. Then, in order to define criteria for measuring each dimension, by providing a comprehensive and combined classification including three domains, user-based analysis, relationship-based analysis and content-based analysis, exploration techniques Social media has been examined to analyze the effective behaviors of users. In the following, according to the literature review, the criteria of "number of active users", “ranked of users based on the structural indexes and activity", “quality and the subjectiveness of content” have been defined to measure each of the aforementioned dimensions. The criteria proposed in this article are effective for creating dashboards to assess the value of users' influence in various businesses. It also a comprehensive roadmap has been provided for businesses about the data they need to collect and the required techniques to determine each of these metrics through a cross-disciplinary and academic classification of social media exploration techniques.
1. Agarwal, N., Kumar, S., Gao, H., Zafarani, R., & Liu, H. (2011). Analyzing Behavior Of The Influentials Across Social Media.
2. Bi, B., Tian, Y., Sismanis, Y., Balmin, A. & Cho. J. (2014). Scalable topic-speci_c inuence analysis on microblogs.
In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, 513-522, New York, NY, USA.
3. Eirinaki, M., Monga, S.,Sundaram, S. (2012). Identification of influential social network users. International Journal of Web Based Communities 8(2),136–158
4. Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nat Phys, 6(11), 888-893. doi: http://www.nature.com/nphys/journal/v6/n11/abs/nphys1746.html#supplementary-information
5. Kumar, V., Bhaskaran, V., Mirchandani, R., Shah,. Milap, S. )2013(. Creating a Measurable Social Media Marketing Strategy: Increasing the Value and ROI of Intangibles and Tangibles for Hokey Pokey. Journal of Marketing Science, 32(2), 194–212.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, Th., & Tillmanns, S. (2010). Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value. Journal of Service Research, 13(3), 297-310.
7. Lerman, K., & Ghosh, R. (2010). Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks. CoRR, abs/1003.2664.
8. Liang, T. P., & Turban, E. (2012). Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce, International Journal of Electronic Commerce, 16(2), 5–13.
9. Libai, B., Muller, E. & Peres, R. (2012). Decomposing the Value of Word-of-Mouth Seeding Programs: Acceleration vs. Expansion, Journal of Marketing Research.
10. Li, Y., Ma, S., Zhang, Y. & Huang, R. (2013). An improved mix framework for opinion leader identification in online learning communities, Knowledge-Based Systems, (43), 43–51.
11. Li, Y.-M., Lai, C.-Y., and Chen, C.-W. (2011). Discovering influencers for marketing in the blogosphere. Inf. Sci., 181(23), 5143-5157.
12. Li, Y.-M., Lin, C.-H., & Lai, C.-Y. 2010. Identifying influential reviewers for word-of-mouth marketing. Electronic Commerce Research and Applications. 9(4), 294-304.
13. Goldenberg,J., Hang, S., Lehmann, D., & Weon Hong, J.(2009). The Role of Hubs in the Adoption Process. Journal of Marketing, 73(2), 1-13
14. Greve,G. (2014). The moderating effect of customer engagement on the brand image – brand loyalty relationship,, 148, 203 – 210¬.
15. Hajli, M. N. (2014). The role of social support on relationship quality and social commerce. Technological Forecasting and Social Change, 87(0), 17-27.
16. Heidemann, J., Klier, M., Probst, F., & Betriebswirtschaftslehr, U. L. f. (2010). Identifying Key Users in Online Social Networks:A PageRank Based Approach.
17. Ilyas, M. U., & Radha, H. (2011). Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality. Paper presented at the Communications (ICC), 2011 IEEE International Conference on
18. Jiliang, T., Yi, Ch. & Huan, L. 2014, Mining Social Media with Social Theories: A Survey, ACM SIGKDD Explorations Newsletters, 15(2), 20-29.
19. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59–68.
20. Katsimpras, G., Vogiatzis, D., & Paliouras, G. (2015). Determining Influential Users with Supervised Random Walks. Paper presented at the Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.
21. Kiss, C., & Bichler, M. (2008). Identification of influencers — measuring influence in customer networks. Decision Support Systems, 46(1), 233-253. doi: http://dx.doi.org/10.1016/j.dss.2008.06.007
22. Munger, T., & Zhao, J. (2015). Identifying Influential Users in On-line Support Forums using Topical Expertise and Social Network Analysis. Paper presented at the Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, Paris, France.
23. Peters, K., Chen, Y., Kaplan, A. M., Ognibeni, B. & Pauwels,K. (2013). Social Media Metrics - A Framework and Guidelines for Managing Social Media, Journal of Interactive Marketing, 27, 281 – 298.
24. Probst, F., Grosswiele, L. & Pflege, R. (2013). Who will lead and who will follow: Identifying Influential Users in Online Social Networks, Business & Information Systems Engineering, (3), DOI 10.1007/s12599-013-0263-7.
25. Raamakirtinan, S. & Livingston, L. M. J. (2016). Identifying Influential Users in Facebook - A Sentiment Based Approach. Indian Journal of Science & technology, 9(10).
26. Saito, K., Kimura, M., Ohara, K., & Motoda, H. (2012). Efficient discovery of influential nodes for SIS models in social networks. Knowledge and Information Systems, 30(3), 613-635. doi: 10.1007/s10115-011-0396-2
27. Yong, S. K. & Tran, V. L. (2013). Assessing the Ripple Effects of Online Opinion Leaders With Trust and Distrust metrics, Expert Systems with Applications, (40), 3500–3511
28. Zafarani, R. & Liu, H., 2014. Behavior Analysis in Social Media, IEEE Intelligent Systems, 29 (4), 1-4.
29. Zhang, Y., Wang, Z., & Xia, C. (2010). Identifying Key Users for Targeted Marketing by Mining Online Social Network. Paper presented at the Proceedings of the 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.
30. Zhao, K., Greer, G. E., Y, J., Mitra, P., Portier, K. (2014). Leader identification in an online health community for cancer survivors: a social network-based classification approach, Information System E-Business Management, DOI 10.1007/s10257-014-0260-5.
31. Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach, Decision Support Systems, 56(0), 211-222.
32. Zolfaghar, K. & Aghaie, A. (2011). A syntactical approach for interpersonal trust prediction in social web applications: Combining contextual and structural data, Knowledge-Based Systems.doi:10.1016/j.knosys.2010.10.07