Investigating Youth Purchase Intentions for Viral Products: A Machine Learning Approach Based on Social Media Influence

Social media has significantly reshaped marketing strategies and consumer behavior over the past decade.This study investigates how social media engagement, advertising strategies, and economic behaviors impact the purchase intentions of students. By employing advanced machine learning techniques and integrating multiple models, the research analyzes these factors' influence on purchasing decisions. It tested eight machine learning models: Logistic Regression , Support Vector Classifier, Gaussian Naive Bayes, Random Forest, Extreme Gradient Boosting, Extra Trees, Gradient Boosting, and Adaptive Boosting..The Random Forest model demonstrated superior performance, achieving an accuracy of 71.2% and an ROC AUC of 79.1%. Feature selection and SHAP analysis were employed to identify critical drivers of purchase intentions. Findings reveal that social media engagement, advertising strategies, and economic behaviors significantly influence purchase decisions. The SHAP analysis highlights that the number of social media platforms used inversely affects viral product purchase intentions, challenging the notion that broader engagement necessarily leads to higher purchasing behavior