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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.15072 |
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| _version_ | 1866913801222225920 |
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| author | Li, Yulong Lu, Zhixiang Tang, Feilong Lai, Simin Hu, Ming Zhang, Yuxuan Xue, Haochen Wu, Zhaodong Razzak, Imran Li, Qingxia Su, Jionglong |
| author_facet | Li, Yulong Lu, Zhixiang Tang, Feilong Lai, Simin Hu, Ming Zhang, Yuxuan Xue, Haochen Wu, Zhaodong Razzak, Imran Li, Qingxia Su, Jionglong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis Li, Yulong Lu, Zhixiang Tang, Feilong Lai, Simin Hu, Ming Zhang, Yuxuan Xue, Haochen Wu, Zhaodong Razzak, Imran Li, Qingxia Su, Jionglong Social and Information Networks Computation and Language 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. |
| title | Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2504.15072 |