Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912810664984576 |
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| author | Tsai, Chung Han Lin, ChengTo Tsai, Chung Han Lin, ChengTo Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia |
| author_facet | Tsai, Chung Han Lin, ChengTo Tsai, Chung Han Lin, ChengTo Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia |
| contents | Amid the rapid digitalization of judicial systems, the integration of big data into adjudication remains underexplored, particularly in uncovering the structural logic of legal applications. This study bridges this gap by employing social network analysis (SNA) to examine credit card disputes involving personal information protection adjudicated in Beijing. By constructing a legal citation network, we reveal the latent patterns of substantive and procedural law application. The findings demonstrate that SNA can effectively identify core legal norms and typify cases, offering a robust methodological framework for optimizing 'Digital Court' systems. These insights provide practical pathways for enhancing judicial efficiency and consistency through data-driven case retrieval and holistic judicial information networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05299 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing Tsai, Chung Han Lin, ChengTo Tsai, Chung Han Lin, ChengTo Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia Zhang, Baowen Deng, Qingyue Zhao, Yunhui Song, Zhijia Social and Information Networks Machine Learning Amid the rapid digitalization of judicial systems, the integration of big data into adjudication remains underexplored, particularly in uncovering the structural logic of legal applications. This study bridges this gap by employing social network analysis (SNA) to examine credit card disputes involving personal information protection adjudicated in Beijing. By constructing a legal citation network, we reveal the latent patterns of substantive and procedural law application. The findings demonstrate that SNA can effectively identify core legal norms and typify cases, offering a robust methodological framework for optimizing 'Digital Court' systems. These insights provide practical pathways for enhancing judicial efficiency and consistency through data-driven case retrieval and holistic judicial information networks. |
| title | Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2601.05299 |