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Social media optimization: Identifying an optimal strategy for increasing network size on Facebook

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  • Ballings, Michel
  • Van den Poel, Dirk
  • Bogaert, Matthias

Abstract

This paper aims to create an expert system that yields an optimal strategy for increasing network size on Facebook. Data were obtained from 5488 Facebook users by means of a custom-built Facebook application. We computed a total of 426 variables. Using these data we estimated a predictive model of network size which is subsequently used in a prescriptive model. The former is estimated with Random Forest and the latter with a Genetic Algorithm. The results indicate that the proposed expert system can identify an optimal social media strategy. The system delivers concrete recommendations about, for example, the optimal time between status updates. The analysis reveals that network size can be increased by 61% if the optimal strategy is adopted. This study contributes to literature in the following two ways. First it devises a novel prescriptive social media expert system relying on an unprecedented variety of social media data. The results indicate that the system is effective and a viable strategic tool for increasing network size. Second it provides a list of the top drivers allowing future research to build similar systems efficiently.

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  • Ballings, Michel & Van den Poel, Dirk & Bogaert, Matthias, 2016. "Social media optimization: Identifying an optimal strategy for increasing network size on Facebook," Omega, Elsevier, vol. 59(PA), pages 15-25.
    • Handle: RePEc:eee:jomega:v:59:y:2016:i:pa:p:15-25
    DOI: 10.1016/j.omega.2015.04.017
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    Cited by:

    1. Cai, Yajun & Wu, Yibin & Xue, Weili, 2024. "Social media retailing in the creator economy," Omega, Elsevier, vol. 124(C).
    2. Ta-Chung Chu & Miroslav Kysely, 2021. "Ranking objectives of advertisements on Facebook by a fuzzy TOPSIS method," Electronic Commerce Research, Springer, vol. 21(4), pages 881-916, December.
    3. Julian Inchauspe, 2021. "Modelling Facebook and Outlook event attendance decisions: coordination traps and herding," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(4), pages 797-815, October.
    4. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    5. Wang, Xu & Baesens, Bart & Zhu, Zhen, 2019. "On the optimal marketing aggressiveness level of C2C sellers in social media: Evidence from china," Omega, Elsevier, vol. 85(C), pages 83-93.
    6. Ionela-Roxana GLAVAN & Andreea MIRICA & Bogdan Narcis FIRTESCU, 2016. "The Use of Social Media for Communication In Official Statistics at European Level," Romanian Statistical Review, Romanian Statistical Review, vol. 64(4), pages 37-48, December.
    7. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.

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