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Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

Author

Listed:
  • Tai Huynh

    (Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
    Kyanon Digital, Ho Chi Minh City 700000, Vietnam
    Equal contribution.)

  • Hien Nguyen

    (Faculty of Computer Science, University of Information Technology, Ho Chi Minh City 700000, Vietnam
    Vietnam National University, Ho Chi Minh City (VNU-HCM), Quarter 6, Thu Duc District, Ho Chi Minh City 700000, Vietnam
    Equal contribution.)

  • Ivan Zelinka

    (Department of Computer Sciences, FEI VBS Technical University of Ostrava Tr. 17. Listopadu 15, Ostrava 70800, Czech Republic
    Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Dac Dinh

    (Kyanon Digital, Ho Chi Minh City 700000, Vietnam)

  • Xuan Hau Pham

    (Faculty of Engineering – Information Technology, Quang Binh University, Dong Hoi City 510000, Quang Binh, Vietnam
    Equal contribution.)

Abstract

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

Suggested Citation

  • Tai Huynh & Hien Nguyen & Ivan Zelinka & Dac Dinh & Xuan Hau Pham, 2020. "Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3064-:d:344176
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    References listed on IDEAS

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    1. Luis Mañas-Viniegra & Ana-Isabel Veloso & Ubaldo Cuesta, 2019. "Fashion Promotion on Instagram with Eye Tracking: Curvy Girl Influencers Versus Fashion Brands in Spain and Portugal," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    2. Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
    3. Ignas Zimaitis & Mindaugas Degutis & Sigitas Urbonavicius, 2020. "Social Media Use and Paranoia: Factors That Matter in Online Shopping," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
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    Cited by:

    1. Quan M. Tran & Hien D. Nguyen & Tai Huynh & Kha V. Nguyen & Suong N. Hoang & Vuong T. Pham, 2022. "Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2919-2945, November.

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