A Framework for Social Media-Driven Predictive Marketing Using Probabilistic Modelling and User Behaviour Mining

In the age of digital transformation, social media has emerged as a powerful tool for marketers to understand, influence, and predict consumer behavior. This study develops a robust framework that leverages social data mining and probabilistic modeling to enhance audiencetargeted marketing. The research integrates user interaction data, sentiment analysis, and network structure to build predictive models capable of identifying high-value targets for online advertising. A distributed Lasso-based regression technique, coupled with Singular Value Thresholding (SVT), is employed to address issues of data sparsity and scalability. Additionally, the system uses probabilistic Bayesian networks to infer social brand reputation and user positivity, thereby enabling the dynamic ranking of products and influencers. Realworld implementation using data from platforms like Pinterest and Facebook validates the system's effectiveness in optimizing marketing campaigns. The findings highlight the importance of combining textual insights with user behavior and network dynamics to achieve precision targeting. This work offers a computational foundation for predictive marketing strategies and emphasizes the growing relevance of big data in marketing intelligence. It serves as a blueprint for businesses aiming to transform vast social data into strategic insights that drive consumer engagement and brand loyalty