A Systematic Analysis of Higher Educational Content Over Social Media for Engagement Optimization

Abstract

Abstract The purpose of this article is to analyze higher educational institutions’ contents in terms of post variables and engagement volume based on categorical classification of theme to understand which factors affect the overall engagement. The sample included 29,814 Facebook, Instagram, and Twitter posts from the top 10 largest and global higher education institutions by community size as of January 1, 2021. The platform’s publically available dashboard metrics were used to analyze the engagement. A negative binomial regression model was used to estimate the impact of selected variables on engagement. Instagram has the highest potential for engagement growth and also dominates the other platforms for engagement per post. Twitter has been observed as the most preferred platform by volume of activity and also the least efficient of all. Facebook has the highest volume of engagement and second-highest efficiency. There is a huge gap between the publisher’s activity priority and engagement pattern across the selected platforms. The findings highlight the importance of developing a systematic procedure for analyzing content engagement potential and designing post strategies for each platform. This study contributes to the literature by designing a framework to analyze post efficiency as per content category for any given platform based on public level data. This adds up to the ability of the competitors with social media to analyze their position in terms of engagement and helps in estimation. These enhancements resulted in a framework with more explanatory power while projecting post efficiency.