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

Michel Ballings, Dirk Van den Poel and Matthias Bogaert

Omega, 2016, vol. 59, issue PA, 15-25

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.

Keywords: Social media; Facebook; Strategy optimization; Predictive modeling; Prescriptive modeling; Random Forest; Genetic Algorithm; Data mining (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.omega.2015.04.017

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