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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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    1. Asllani, Arben & Lari, Alireza, 2007. "Using genetic algorithm for dynamic and multiple criteria web-site optimizations," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1767-1777, February.
    2. Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
    3. Chen, Mu-Chen & Wu, Hsiao-Pin, 2005. "An association-based clustering approach to order batching considering customer demand patterns," Omega, Elsevier, vol. 33(4), pages 333-343, August.
    4. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
    5. Li, Gang & Jiang, Hongxun & He, Tian, 2015. "A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem," Omega, Elsevier, vol. 50(C), pages 1-17.
    6. Deane, Jason & Agarwal, Anurag, 2012. "Scheduling online advertisements to maximize revenue under variable display frequency," Omega, Elsevier, vol. 40(5), pages 562-570.
    7. Deza, Antoine & Huang, Kai & Metel, Michael R., 2015. "Chance constrained optimization for targeted Internet advertising," Omega, Elsevier, vol. 53(C), pages 90-96.
    8. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    9. Blazewicz, Jacek & Pesch, Erwin & Sterna, Malgorzata, 2005. "A novel representation of graph structures in web mining and data analysis," Omega, Elsevier, vol. 33(1), pages 65-71, February.
    10. Steiner, Maria Teresinha Arns & Datta, Dilip & Steiner Neto, Pedro José & Scarpin, Cassius Tadeu & Rui Figueira, José, 2015. "Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil," Omega, Elsevier, vol. 52(C), pages 53-64.
    11. Moorthy, Sridhar & Hawkins, Scott A., 2005. "Advertising repetition and quality perception," Journal of Business Research, Elsevier, vol. 58(3), pages 354-360, March.
    12. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.
    13. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    14. Hemant Ishwaran & Eugene H. Blackstone & Claire E. Pothier & Michael S. Lauer, 2004. "Relative Risk Forests for Exercise Heart Rate Recovery as a Predictor of Mortality," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 591-600, January.
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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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