{"type":"ScholarlyArticle","title":"CasUGC: Aligning User-Generated Comment Evolution with Cascade Dynamics for Popularity Prediction","authors":[{"name":"Yifan Meng","slug":"yifan-meng","given":"Yifan","family":"Meng","affiliation":""},{"name":"Xigang Sun","slug":"xigang-sun","given":"Xigang","family":"Sun","affiliation":""},{"name":"Anran Zhang","slug":"anran-zhang","given":"Anran","family":"Zhang","affiliation":""},{"name":"Jiaqi Jiang","slug":"jiaqi-jiang","given":"Jiaqi","family":"Jiang","affiliation":""},{"name":"Jiahui Jin","slug":"jiahui-jin","given":"Jiahui","family":"Jin","affiliation":""}],"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.\r\nHowever, existing methods primarily focus on learning structural–temporal cascade dynamics while neglecting the modeling of user-generated comment evolution, leading to suboptimal predictions.\r\nIn this paper, we propose a novel framework, CasUGC, that aligns Cascade dynamics with User-Generated Comment evolution for popularity prediction.\r\nSpecifically, 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. \r\nTo 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.\r\nExtensive experiments conducted on four real-world social media datasets demonstrate the superiority of CasUGC over state-of-the-art methods.","doi":"10.24963/ijcai.2026/626","url":"https://ijcai.org/proceedings/2026/626","pdf":"https://ijcai.org/proceedings/2026/0626.pdf","markdown":"https://ijcai.org/proceedings/2026/626.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/626","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":626,"pages":{"first":5621,"last":5629},"keywords":["Multidisciplinary Topics and Applications: Web and social networks"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}