---
title: "CasUGC: Aligning User-Generated Comment Evolution with Cascade Dynamics for Popularity Prediction"
authors:
  - "Yifan Meng"
  - "Xigang Sun"
  - "Anran Zhang"
  - "Jiaqi Jiang"
  - "Jiahui Jin"
doi: "10.24963/ijcai.2026/626"
url: "https://ijcai.org/proceedings/2026/626"
pdf: "https://ijcai.org/proceedings/2026/0626.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5621-5629"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: Web and social networks"
---

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

Yifan Meng, Xigang Sun, Anran Zhang, Jiaqi Jiang, Jiahui Jin

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5621-5629. DOI: [10.24963/ijcai.2026/626](https://doi.org/10.24963/ijcai.2026/626)

## 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.