用AI系统实时预测AIGC传播与市场增长，提升营销决策效率
AI-Integrated Decision Support System for Real-Time Market Growth Forecasting and Multi-Source Content Diffusion Analytics
- 融合多源数据的图神经网络与时间注意力框架，联合建模内容传播与影响演化
- 在六个指标上优于基线模型，可精准追踪AIGC对市场可见度和投资回报的影响
- 适合数字营销、平台运营与数据驱动决策者快速理解内容扩散规律
AI生成内容（AIGC）的快速普及重塑了数字营销与线上消费者行为。然而，由于数据异构性、非线性传播机制及动态用户互动，预测其传播轨迹与市场影响仍具挑战。本文提出一个集成多源数据的AI驱动决策支持系统（DSS），包括社交媒体流、营销支出记录、用户参与日志与情感动态，采用混合图神经网络（GNN）与时间变换器框架。模型通过双通道架构联合学习内容传播结构与时序影响力演化，并利用因果推断模块解耦营销刺激对投资回报率（ROI）与市场可见性的影响。在来自Twitter、TikTok与YouTube广告的多个真实平台大规模数据集上实验表明，该系统在全部六个指标上均优于现有基线。所提DSS通过提供可解释的实时洞察，增强AIGC驱动的内容传播与市场增长决策能力。
原文摘要 · Abstract (English)
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.
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