Prediction of Social Media Virality Through Stacking Ensemble utilising ML Algorithms

The user interaction datasets are too complex with a lot of noise which makes predicting virality with social media content extremely difficult. This multi-faceted problem requires different models to come up with a solution, however, most single-model approaches are proving to be inefficient due to the lack of accuracy and reliability in predictive performance. This research comes up with a new stacking ensemble model that incorporates Random Forest, XGBoost, and Logistic Regression, which aims to solve the problem stated before. The main focus is improving model robustness while retaining a clear interpretive framework and incorporating weakly labeled dataset interpretability. Employing bagging, boosting, and probabilistic calibration simultaneously improves accuracy and reliability without sacrificing ensemble bias. The model was viral content detected at 95% accuracy, 98% F1-score, with 90% precision and recall, and 92% ROC-AUC showcasing excellent metrics predictive performance. Improved prediction reliability was ensured through calibrated outputs alignment by regression with real-world distribution alignment enhancing predictive dependability. This model provides strong efficient scalable structure to prediction virality and emphasizes its potential across diverse data-centric domains.