Platform Architecture

The core architecture of this marketing intelligence platform is fundamentally inspired by the MGLEP (Multimodal Graph Learning for Modeling Emerging Pandemics) research framework, strategically adapted and customized for industrial social media ecosystem applications. While the original MGLEP was developed to track pandemic dynamics through multi-modal data integration, our solution transposes its sophisticated temporal graph neural network approach to decode complex social media interaction landscapes. The key transformation involves redirecting the framework's predictive capabilities from epidemiological trend analysis to marketing intelligence, effectively leveraging the same principles of dynamic graph learning, semantic feature extraction, and multi-source data fusion. By maintaining the core architectural strengths of MGLEP - such as pre-trained language model embeddings, adaptive graph convolution, and recurrent neural network learning - we've created a robust, flexible platform that can capture nuanced audience behaviors, predict content performance, and provide actionable marketing insights. This approach represents a paradigm shift from traditional social media analytics, offering a more intelligent, predictive, and contextually rich understanding of digital marketing dynamics. Here are key solutions of our SociAI’s architecture:

• Multi-modal data integration framework leveraging temporal graph neural networks

• Modular design with three primary data sources: a) Core statistical metrics b) Government response/regulation data c) Social media interaction graph

• Utilizes pre-trained language models (specifically BertTweet) for semantic feature extraction

• Employs graph convolution and recurrent neural network architectures for dynamic learning

• The platform leverages state-of-the-art Large Language Models (LLMs) to provide dynamic, context-aware marketing intelligence that can seamlessly adapt across different industry verticals.