Detecting Influencers in Social Media Using Social Network Analysis (SNA)

  • Authors

    • Siti Nurulain Mohd Rum
    • Razali Yaakob
    • Lilly Suriani Affendey
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.27615
  • Social Network Analysis (SNA), Social Media, Centrality Measurements, Digital Influencer

  • Social media has now become a key part of life in modern society; it is a place where people share their ideas, view, emotions, and sentiments. The explosion in the popularity of social media has led to an immense increase in data over the past few years. Users engage with this platform to share their experiences, feelings, and opinions on a broad range of topics, such as politics, personalities, news, products or events. Social media has also become a phenomenal platform that provides a powerful way for businesses to enhance their prospects and reach customers. Extracting and interpreting information from user-generated content is a trending topic in the scientific community as well as in the business world, and has attracted the interest of many commercial organizations. With the wise use of social media, the marketing process for promoting products and brands can be accelerated to reach the target audience. The beauty and health industry is one of the industries that make use of this platform as their digital marketing solution to integrate communications. Recently, many leading companies and brands have used digital influencers as their strategy for marketing campaigns in management and development. Therefore, the analysis of information extracted from social media is of great importance offering valuable insights and where the importance of each actor or individual in social media can be identified.  This can be achieved through the use of Social Network Analysis (SNA).  This research work aims at probing the effectiveness of SNA in social media in detecting the influencers in the area of beauty and health.

     

     

     

  • References

    1. [1] Bakshy, Eytan, Hofman, Jake M, Mason, Winter A, & Watts, Duncan J. 2011. Everyone's an influencer: quantifying influence on twitter. Proceedings of the fourth ACM international conference on Web search and data mining.

      [2] Bloch, Francis, Jackson, Matthew O, & Tebaldi, Pietro. 2016. Centrality measures in networks.

      [3] Borgatti, Stephen P. 2006. Identifying sets of key players in a social network. Computational & Mathematical Organization Theory, 12(1), 21-34.

      [4] Brandes, Ulrik. 2001. A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177.

      [5] Brandes, Ulrik, Borgatti, Stephen P, & Freeman, Linton C. 2016. Maintaining the duality of closeness and betweenness centrality. Social Networks, 44, 153-159.

      [6] Cha, Meeyoung, Haddadi, Hamed, Benevenuto, Fabricio, & Gummadi, P Krishna. 2010. Measuring user influence in twitter: The million follower fallacy. Icwsm, 10(10-17), 30.

      [7] Easley, David, & Kleinberg, Jon. 2010. Networks, crowds, and markets. Cambridge Univ Press, 6(1), 1-6.

      [8] Fraser, Benson P, & Brown, William J. 2002. Media, celebrities, and social influence: Identification with Elvis Presley. Mass Communication & Society, 5(2), 183-206.

      [9] Freeman, Linton C. 1978. Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.

      [10] Gayo-Avello, Daniel. 2013. Nepotistic relationships in twitter and their impact on rank prestige algorithms. Information Processing & Management, 49(6), 1250-1280.

      [11] Keller, Edward, & Berry, Jonathan. 2003. The influentials: One American in ten tells the other nine how to vote, where to eat, and what to buy: Simon and Schuster.

      [12] Kiss, Christine, & Bichler, Martin. 2008. Identification of influencers—measuring influence in customer networks. Decision Support Systems, 46(1), 233-253.

      [13] Kozinets, Robert V, De Valck, Kristine, Wojnicki, Andrea C, & Wilner, Sarah JS. 2010. Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of marketing, 74(2), 71-89.

      [14] Landherr, Andrea, Friedl, Bettina, & Heidemann, Julia. 2010. A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2(6), 371-385.

      [15] Mehra, Ajay. 2005. The Development of Social Network Analysis: A Study in the Sociology of Science: SAGE Publications Sage CA: Los Angeles, CA.

      [16] Scott, John. 2017. Social network analysis: Sage.

      [17] Valente, Thomas W, Coronges, Kathryn, Lakon, Cynthia, & Costenbader, Elizabeth. 2008. How correlated are network centrality measures? Connections (Toronto, Ont.), 28(1), 16.

      [18] Weimann, Gabriel. 1991. The influentials: back to the concept of opinion leaders? Public Opinion Quarterly, 55(2), 267-279.

      [19] Wildemuth, Barbara M. 2016. Applications of social research methods to questions in information and library science: ABC-CLIO.

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  • How to Cite

    Nurulain Mohd Rum, S., Yaakob, R., & Suriani Affendey, L. (2018). Detecting Influencers in Social Media Using Social Network Analysis (SNA). International Journal of Engineering & Technology, 7(4.38), 950-954. https://doi.org/10.14419/ijet.v7i4.38.27615