Title: Improving social media engagements on paid and non-paid advertisements: a data mining approach
Authors: Jen-Peng Huang; Genesis Sembiring Depari
Addresses: Information Management Department, Southern Taiwan University of Science and Technology, No. 1, Nan-Tai Street, Yungkang Dist., Tainan City 710, Taiwan ' Business and Management Department, Southern Taiwan University of Science and Technology, No. 1, Nan-Tai Street, Yungkang Dist., Tainan City 710, Taiwan
Abstract: The purpose of this research is to develop a strategy to improve the number of social media engagement on Facebook both for paid and non-paid publications through a data mining approach. Several Facebook post characteristics were weighted in order to rank the input variables importance. Three machine learning algorithms performance along with dynamic parameters were compared in order to obtain a robust algorithm in assessing the importance of several input factors. Random forest is found as the most powerful algorithm with 79% accuracy and therefore used to analyse the importance of input factors in order to improve the number of engagements of social media posts. Eventually, total page likes (number of page follower) of a company Facebook page are found as the most important factor in order to have more social media engagements both for paid and non-paid publications. We also propose a managerial implication on how to improve the number of engagements in company social media.
Keywords: social media; data mining; paid advertisement; non-paid advertisement; social media engagements.
International Journal of Data Analysis Techniques and Strategies, 2021 Vol.13 No.1/2, pp.88 - 106
Received: 12 Dec 2018
Accepted: 03 May 2019
Published online: 30 Apr 2021 *