CasUGC: Aligning User-Generated Comment Evolution with Cascade Dynamics for Popularity Prediction

Abstract

Accurately predicting the popularity of information cascades facilitates the development of social media network applications. During the cascade propagation process, user-generated comments continually evolve, thereby substantially affecting overall information popularity.

However, existing methods primarily focus on learning structural–temporal cascade dynamics while neglecting the modeling of user-generated comment evolution, leading to suboptimal predictions.

In this paper, we propose a novel framework, CasUGC, that aligns Cascade dynamics with User-Generated Comment evolution for popularity prediction.

Specifically, we develop a dual-granularity alignment strategy that bridges the representation gap between comment semantics and structural–temporal dynamics at both the user and cascade levels. Building on this alignment, we further design a dynamic cascade feature generation module to produce temporally enhanced cascade embeddings.

To further address the inherent uncertainties in both alignment and prediction, we introduce two diffusion-based components: a cascade-level diffusion network that learns a latent distribution mapping between semantic and structural–temporal representations, and a second diffusion process that captures the stochasticity of propagation dynamics for robust prediction.

Extensive experiments conducted on four real-world social media datasets demonstrate the superiority of CasUGC over state-of-the-art methods.