用图神经网络与霍克斯过程分析评论层级中的意见传播动态
Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis
- 结合多维霍克斯过程与图神经网络建模意见传播
- 在包含47万贴文的VISTA数据集上实现高精度传播预测
- 适合研究社交媒体舆论演化与情感传播的学者
社交媒体的快速发展重塑了公众意见的传播动态，传统模型难以有效捕捉其复杂交互。为此，本文提出一种创新方法，将多维霍克斯过程与图神经网络相结合，建模社交网络中节点间的意见传播动态，并考虑评论的复杂层级关系。扩展的多维霍克斯过程能捕捉层级结构、多维交互及不同主题间的相互影响，形成复杂的传播网络。此外，针对高质量公共意见演化数据集的缺乏，本文构建新数据集VISTA，涵盖159个热门话题，对应47,207篇帖子、327,015条二级评论和29,578条三级评论，覆盖政治、娱乐、体育、健康等多个领域。数据集标注了11类细粒度情感标签，并明确界定评论层级关系。该方法结合数据集可实现强可解释性，将情感传播与评论层级及时间演化关联。本工作为后续研究提供可靠基准。
原文摘要 · Abstract (English)
The rapid development of social media has significantly reshaped the dynamics of public opinion, resulting in complex interactions that traditional models fail to effectively capture. To address this challenge, we propose an innovative approach that integrates multi-dimensional Hawkes processes with Graph Neural Network, modeling opinion propagation dynamics among nodes in a social network while considering the intricate hierarchical relationships between comments. The extended multi-dimensional Hawkes process captures the hierarchical structure, multi-dimensional interactions, and mutual influences across different topics, forming a complex propagation network. Moreover, recognizing the lack of high-quality datasets capable of comprehensively capturing the evolution of public opinion dynamics, we introduce a new dataset, VISTA. It includes 159 trending topics, corresponding to 47,207 posts, 327,015 second-level comments, and 29,578 third-level comments, covering diverse domains such as politics, entertainment, sports, health, and medicine. The dataset is annotated with detailed sentiment labels across 11 categories and clearly defined hierarchical relationships. When combined with our method, it offers strong interpretability by linking sentiment propagation to the comment hierarchy and temporal evolution. Our approach provides a robust baseline for future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。