Estimating the causal effect of timing on the reach of social media posts

Lauri Valkonen (), Jouni Helske () and Juha Karvanen ()
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Lauri Valkonen: University of Jyväskylä
Jouni Helske: University of Jyväskylä
Juha Karvanen: University of Jyväskylä

Statistical Methods & Applications, 2023, vol. 32, issue 2, No 7, 493-507

Abstract: Abstract Modern companies regularly use social media to communicate with their customers. In addition to the content, the reach of a social media post may depend on the season, the day of the week, and the time of the day. We consider optimizing the timing of Facebook posts by a large Finnish consumers’ cooperative using historical data on previous posts and their reach. The content and the timing of the posts reflect the marketing strategy of the cooperative. These choices affect the reach of a post via a dynamic process where the reactions of users make the post more visible to others. We describe the causal relations of the social media publishing in the form of a directed acyclic graph, use an identification algorithm to obtain a formula for the causal effect, and finally estimate the required conditional probabilities with Bayesian generalized additive models. As a result, we obtain estimates for the expected reach of a post for alternative timings.

Keywords: Causal inference; Bayesian model; Social media; Decision making (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-022-00664-z

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