The exponential growth of social media has positioned user-generated content as a rich yet underexploited resource for understanding collective human behaviour, opinion dynamics, and information propagation. Existing analytical solutions are largely confined to individual platforms and often rely on opaque machine-learning pipelines, limiting transparency, reproducibility, and regulatory compliance. This work presents a novel API-driven social media intelligence framework that integrates heterogeneous data from Twitter, Reddit, and YouTube into a unified analytical pipeline. The proposed architecture synthesises three analytical dimensions: semantic text understanding through Natural Language Processing (NLP), structural interaction modelling via graph-theoretic methods, and decision transparency through Explainable Artificial Intelligence (XAI). A layered, modular design addresses the dual challenges of data heterogeneity and ethical governance. Empirical evaluation confirms that cross-platform data fusion yields measurably superior analytical stability and reduced platform-induced bias relative to single-source baselines. Beyond its research contributions, the framework is deliberately architected to serve as a deployable foundation for a final-year academic project.
Ayush Pravin Kudale (Thu,) studied this question.