Abstract
Social networks, as abstract representations of relationships between entities, play a pivotal role in connecting individuals in the digital age. This paper delves into the realm of social network analysis (SNA), a method rooted in graph theory that explores the dynamics of social relationships within communities. One of the key objectives of SNA is to identify the most influential actors within a social network, a task often achieved by calculating various centrality metrics. These metrics, such as degree centrality, allow to quantify the significance and impact of individual nodes within a social network. In the context of marketing and brand promotion, these metrics are particularly relevant and useful. Leveraging social networks for marketing endeavors can enhance brand recognition and foster customer loyalty. When promoting products within social networks, targeting the “most important” members (i.e., those with higher centrality metrics) can exponentially increase the reach and impact of the marketing campaign. In this paper, an approach is suggested for supporting social network marketing by employing binary logistic regression analysis. Logistic regression is a valuable tool in case of models where the dependent variable is dichotomous, and it can be an ideal method for predicting node behavior in the context of a product promotion campaign. By analyzing the actions of the “most important” nodes within social networks, we can predict which nodes are likely to purchase a marketed product based on their interactions and centrality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Social networking ads. https://www.kaggle.com/datasets/sonalisingh1411/-social-networking-ads/data. Accessed on 10 Oct 2023
Top 100 instagram users by followers. https://www.socialtracker.io/toplists/top-100-instagram-users-by-followers/. Accessed 10 Oct 2023
Allaymoun, M.H., Hamid, O.A.H.: Business intelligence model to analyze social network advertising. In: IEEE International Conference on Information Technology (ICIT), pp. 326–330 (2021)
Arroyo, D.O.: Discovering sets of key players in social networks. In: Computational Social Network Analysis – Trends. Tools and Research Advances, pp. 27–47. Springer, Computer Communications and Networks (2010)
Bhadoria, R.S., Bhoj, N., Srivastav, M.K., Kumar, R., Raman, B.: A machine learning framework for security and privacy issues in building trust for social networking. Cluster Comput. 26(6), 3907–3930 (2022)
Czepiel, S.A.: Maximum likelihood estimation of logistic regression models: Theory and implementation. czep.net/stat/mlelr.pdf 83 (2002)
Das, K., Samanta, S., Pal, M.: Study on centrality measures in social networks: a survey. Soc. Netw. Anal. Min. 8, 1–11 (2018)
Deo, N.: Graph Theory with Applications to Engineering and Computer Science. Courier Dover Publications (2017)
Hosmer, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. John Wiley & Sons (2013)
Iglesias, J.A., Garcia-Cuerva, A., Ledezma, A., Sanchis, A.: Social network analysis: Evolving twitter mining. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1809–1814 (2016)
Kafeza, E., Kanavos, A., Makris, C., Pispirigos, G., Vikatos, P.: T-PCCE: twitter personality based communicative communities extraction system for big data. IEEE Trans. Knowl. Data Eng. 32(8), 1625–1638 (2020)
Kafeza, E., Kanavos, A., Makris, C., Vikatos, P.: T-PICE: twitter personality based influential communities extraction system. In: IEEE International Congress on Big Data, pp. 212–219 (2014)
Kanavos, A., Vonitsanos, G., Mylonas, P.: Clustering high-dimensional social media datasets utilizing graph mining. In: IEEE International Conference on Big Data, pp. 3871–3880 (2022)
Knoke, D.: Origins of social network analysis. In: Encyclopedia of Social Network Analysis and Mining, 2nd edn. Springer (2018)
Knoke, D., Yang, S.: Social Network Analysis. SAGE Publications (2019)
Lidwina, G.J., Vaidegi, T., Hemalatha, S.: Media advertising: a study on advertising on social media. Int. J. Sales Market. Manag. Res. Dev. (IJSMMRD) 10(1), 93–104 (2020)
Mitchell, T.M.: Machine learning, International Edition. McGraw-Hill Series in Computer Science, McGraw-Hill (1997)
Srivastav, M.K., Nath, A.: Study on mathematical modeling of social networks. Int. J. Emerg. Technol. Adv. Eng. 5(3), 611–618 (2015)
Tripiawan, W., Amani, H., Wijaya, A.T.: Effectiveness analysis of social media ads as a promotional media (case study: Instagram Taya.Id). IOP Conf. Ser.: Mater. Sci. Eng. 505(1), 012095 (2019)
Tsvetovat, M., Kouznetsov, A.: Social Network Analysis for Startups: Finding Connections on the Social Web. O’Reilly Media, Inc. (2011)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)
Yousef, M., Dietrich, T., Rundle-Thiele, S.: Social advertising effectiveness in driving action: a study of positive, negative and coactive appeals on social media. Int. J. Env. Res. Public Health 18(11), 5954 (2021)
Zhao, C.: Research on the path to enhance the brand value based on social network. In: 2nd IEEE International Conference on E-Commerce and Internet Technology (ECIT), pp. 130–133 (2021)
Zheng, A., Casari, A.: Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly Media, Inc. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Srivastav, M.K. et al. (2024). Utilizing Degree Centrality Measures for Product Advertisement in Social Networks. In: Papadaki, M., Themistocleous, M., Al Marri, K., Al Zarouni, M. (eds) Information Systems. EMCIS 2023. Lecture Notes in Business Information Processing, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-56481-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-56481-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56480-2
Online ISBN: 978-3-031-56481-9
eBook Packages: Computer ScienceComputer Science (R0)