AI-Integrated Decision Support System for Real-Time Market Growth Forecasting and Multi-Source Content Diffusion Analytics
This addresses the problem of real-time forecasting for AI-generated content diffusion and market growth for digital marketers, though it appears incremental as it builds on existing GNN and Transformer methods.
This study tackled the challenge of predicting AI-generated content diffusion and market impact by proposing an AI-driven Decision Support System that integrates multi-source data using a hybrid GNN and Temporal Transformer framework. The system outperformed existing baselines on all six metrics in experiments on real-world datasets from platforms like Twitter, TikTok, and YouTube.
The rapid proliferation of AI-generated content (AIGC) has reshaped the dynamics of digital marketing and online consumer behavior. However, predicting the diffusion trajectory and market impact of such content remains challenging due to data heterogeneity, non linear propagation mechanisms, and evolving consumer interactions. This study proposes an AI driven Decision Support System (DSS) that integrates multi source data including social media streams, marketing expenditure records, consumer engagement logs, and sentiment dynamics using a hybrid Graph Neural Network (GNN) and Temporal Transformer framework. The model jointly learns the content diffusion structure and temporal influence evolution through a dual channel architecture, while causal inference modules disentangle the effects of marketing stimuli on return on investment (ROI) and market visibility. Experiments on large scale real-world datasets collected from multiple online platforms such as Twitter, TikTok, and YouTube advertising show that our system outperforms existing baselines in all six metrics. The proposed DSS enhances marketing decisions by providing interpretable real-time insights into AIGC driven content dissemination and market growth patterns.