Rhythm of Opinion: Interpretable Hawkes-Graph Networks for Hierarchical Opinion Propagation

Opinion propagation research primarily focuses on phenomenon prediction rather than mechanism understanding, lacking interpretable frameworks to reveal underlying propagation dynamics. This limitation stems from two sources: existing methods employ end-to-end paradigms where parameters lack physical meanings, while available datasets suffer from incomplete hierarchical structures, coarse sentiment annotations, and limited domain coverage. To address these limitations, we introduce VISTA, a multi-dimensional opinion propagation dataset providing complete hierarchical structures, fine-grained emotional annotations, and cross-domain coverage. Based on this dataset, we propose an interpretable modeling framework integrating high-dimensional Hawkes processes with graph neural networks, enabling parametric expression of propagation mechanisms through event space constructed from emotional and reply level combinations. Through interpretable parameter analysis, we reveal three mechanistic patterns: differential emotional propagation strength, asymmetric hierarchical excitation, and temporal memory effects. Our framework establishes quantitative foundations for understanding opinion propagation dynamics, achieving best performance in sentiment prediction and structural consistency tasks while providing the first benchmark for multi-dimensional propagation mechanism analysis.

Citation

Y. Li, Z. Lu, P. Guo, S. Lai, Y. Zhang, H. Xue , et al., "Rhythm of Opinion: Interpretable Hawkes-Graph Networks for Hierarchical Opinion Propagation," 2026, pp. 4611-4622.

Source

WWW '26: Proceedings of the ACM Web Conference 2026

Conference

ACM Web Conference 2026

Keywords

46 Information and Computing Sciences, 4611 Machine Learning

Source

ACM Web Conference 2026

Publisher

Association for Computing Machinery

DOI

10.1145/3774904.3792303

Additional links

https://dl.acm.org/doi/10.1145/3774904.3792303