LEVERAGING SOCIAL MEDIA METRICS IN IMPROVING SOCIAL MEDIA PERFORMANCES THROUGH ORGANIC REACH: A DATA MINING APPROACH
LEVERAGING SOCIAL MEDIA METRICS IN IMPROVING SOCIAL MEDIA PERFORMANCES THROUGH ORGANIC REACH: A DATA MINING APPROACH
Author(s): JEN-PENG HUANG, GENESIS SEMBIRING DEPARI, SRI VANDAYULI RIORINI, PAI-CHOU WANGSubject(s): Methodology and research technology, Social Informatics, ICT Information and Communications Technologies
Published by: Editura Universităţii »Alexandru Ioan Cuza« din Iaşi
Keywords: social media; data mining; organic reach; Facebook pages; Rapidminer;
Summary/Abstract: This paper identified the relevance of several publication’s characteristics of each publication in reaching more people through organic strategy using Support Vector Machines. Before finding the relevance of several inputs, 10 potential models were examined. Based on the results of 10 models examination, we found that Comments, Likes and Shares have smallest error. However, those variables represent the customer engagements, instead of reaching more people. In the other side, Lifetime total organic reach is the best model compares to other models, therefore lifetime total organic reach was selected as a model. Furthermore, page total likes were found as the most relevance input in reaching more people through organic reach. The next most relevance inputs were followed by Type, month, day and hour of publication. Eventually, we come up with a managerial implication on how to publish a post in order to reach more people through organic reach.
Journal: Review of Economic and Business Studies (REBS)
- Issue Year: 2018
- Issue No: 22
- Page Range: 33-48
- Page Count: 16
- Language: English
