"AI-Powered Modeling for Behavioral Forecasting: A Hybrid Approach to Social Media User Behavior Analysis"

Abstract

The boom of social Networking sites generates tons of data also known as rich, massive and social diffused data for user preference, sentiment and behavior capturing. The hybrid approach proposed in this proposal aims to improve social media user behavior prediction ability. Random Forest realize feature importance rank and dimension reduction whereas Long Short-Term Memory (LSTM) model uses the temporal behavior. The web data work as a fuel of the processing pipeline with fetching from Twitter, and Instagram "Realtime" up to submodule for NLP preprocessing of data and training of supervised models. Based on comprehensive performance metrics, the proposed ensemble model based on Random Forest and Long Short-Term Memory networks (RF-LSTM) yields significant superiority to feature-based single classifiers with respect to accuracy, F1-Score and RMSE for trends in user engagement expression prediction as well as that of sentiment polarity and interaction patterns. Our findings highlight how the feature selection step using a Random Forest, can improve the performance of LSTM on high dimensional noisy datasets (such as social media data). The hybrid framework further enhances generalization, interpretability and thus becomes suitable for deployment in dynamic digital environments. Besides, certain scenarios for human anticipative perturbation detection in SNS targeted advertisement, policy monitoring and psychological prevention could be on the top of the design compositions to predict if a user will elaborate any reaction regarding their behavior via digital well-being service. Our approach provides an end-to-end solution that is widely validated and applicable across all behavioral domains, unlike this line of work.