Empirical Analysis of Social Media Interaction Metrics and Their Impact on Startup Engagement

Abstract

In the digital economy, social media serves as a crucial platform for startups to build relationships with audiences and strengthen brand presence. However, the specific effects of different types of user interactions—likes, comments, and shares—on startup engagement remain insufficiently quantified. This study provides an empirical analysis of how social media interaction metrics influence engagement using secondary data from the publicly available Social Media Engagement Metrics dataset on Kaggle. Employing a quantitative design, the study integrates descriptive statistics, Pearson correlation, Random Forest, and multiple linear regression to examine both linear and non-linear relationships. Results show that likes, comments, and shares collectively affect engagement rates, with Random Forest identifying likes as the most influential feature. However, regression results indicate that shares exert a statistically significant but negative effect on engagement, suggesting complex behavioral patterns behind user interactions. Visual analyses—including histograms, boxplots, and heatmaps—support data normality and highlight variation in post performance. The findings emphasize the importance of visually engaging content and interactive captions to enhance user engagement. This study contributes to digital marketing research by combining methodological rigor with actionable insights, offering data-driven recommendations for startups aiming to optimize their social media strategies.