How Data Mining is Used in Social Media. Key Performance Indicators’ Impact on Image Post Data Characteristics for Maximum User Engagement

Abstract

Digital marketing strategy has become increasingly popular aiming to increase social media users’ engagement, brand awareness, and revenues. The aim of this study is to calculate the organic photo posts’ text characteristics such as text readability, hashtags number and characters number. Using data mining classification models, the current study examines whether these characteristics affect organic post user engagement for lifetime post engaged users and lifetime people who have liked a page and engaged with a post. Data were extracted from social media retail business pages. Readability performance metrics (e.g., the post text readability score, the characters’ number, and the hashtags’ number) are the independent variables. Posts’ performances were measured by seven performance metrics, assigned as the depended variables. Data, content characteristics, and performance metrics were extracted from a social platform retail business page. Finally, user engagement was calculated, and posts’ performance classification was represented using decision tree (DTs) graphs. The findings reveal how post texts’ content characteristics impact performance metrics helping this way the marketers to better form their social media organic strategies, the company to increase impressions, reach and revenues, and the customers to comprehend the post message and engage with the brand.