Social Media Diffusion Modeling: Validating Social Media Signals for E-commerce Trend Prediction

In today's digital age, social media has become an indispensable link between consumers and brands. With the popularity of the Internet and the vigorous development of social media platforms, e-commerce enterprises have set their sights on this new and dynamic marketing field, hoping to enhance brand influence, expand user groups and ultimately achieve sales growth through the power of social media. This article systematically reviews the methods to build social media information diffusion model. Focus on prediction model, network analysis and time series modeling as well as compare the traditional modeling and deep learning method. This article also discusses Hawkes process; survival analysis and other time series modeling techniques apply scenarios. The performance of the model is evaluated through indicators such as computational complexity and interpretability, and a hybrid architecture as well as online learning mechanism are proposed to address the challenges of unsteady data. In addition, it emphasized future directions such as multimodal fusion, cross-platform generalization, and privacy protection, and called for the establishment of standardized tools and benchmark datasets to promote the development of the field.