Data-driven methodology for identifying the best influencers for a brand: A case study on Anemonia

Abstract: This study aims to develop a new data-driven methodology for identifying suitable influencers for a brand using data from social media. The increasing presence of such figures in these communication channels makes it challenging to select consistent and influential influencers for a specific audience. This paper introduces an innovative approach to defining these figures based on the analysis of relationships within the brand's network. Specifically, this methodology will be applied to the case study of a brand named “Anemonia”. The approach relies on the sequential application of various steps, including the use of tools such as Social Network Analysis (SNA) centrality, Sentiment Analysis (SA), and Analytical Hierarchical Process (AHP). Through the application of this methodology, the brand has been able to identify influencers consistent with its aesthetics and vision.

How to cite this paper

 Fiocco, E. (2025). Data-driven methodology for identifying the best influencers for a brand: A case study on Anemonia.Management Science Letters , 15(1), 23-30.

Refrences

Agarwal, S., & Damle, M. (2020). Sentiment analysis to evaluate influencer marketing: Exploring to identify the parame-ters of influence. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(6), 4784-4800.
Belanche, D., Casaló, L. V., Flavián, M., & Ibáñez-Sánchez, S. (2021). Understanding influencer marketing: The role of congruence between influencers, products and consumers. Journal of Business Research, 132, 186-195.
Carrington, P. J., Scott, J., & Wasserman, S. (Eds.). (2005). Models and methods in social network analysis (Vol. 28). Cambridge university press.
Dhanesh, G. S., & Duthler, G. (2019). Relationship management through social media influencers: Effects of followers’ awareness of paid endorsement. Public relations review, 45(3), 101765.
Freeman, L. C. (2002). Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociol-ogy. Londres: Routledge, 1, 238-263
Gräve, J. F., & Greff, A. (2018, July). Good KPI, good influencer? Evaluating success metrics for social media influenc-ers. In Proceedings of the 9th International Conference on Social Media and Society (pp. 291-295).
Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).
Lee, D., Lee, S., & Park, S. (2019). A study on influencer characteristic factors by using AHP. Journal of the Society of Korea Industrial and Systems Engineering, 42(3), 184-1
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams en-gineering journal, 5(4), 1093-1113.
Saaty, T. L. (1990). Multicriteria decision making: The analytic hierarchy process: planning, priority setting, resource al-location, 2, 1-20.
Tan, W. B., & Lim, T. M. (2021, October). A study on the centrality measures to determine social media influencers in twitter. In International Conference on Digital Transformation and Applications (ICDXA) (Vol. 25, p. 26).
Tan, W. B., & Lim, T. M. (2022). A study on the centrality measures to determine social media influencers of food-beverage products in Twitter.