Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial-temporal-semantic features
文献类型：期刊论文
| 作者 | Han, Xuehua1; Wang, Juanle2,3; Zhang, Xiaodong1; Wang, Liang1; Xu, Dandan1 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2024-01-02 |
| DOI | 10.1111/tgis.13125 |
| 产权排序 | 1 |
| 英文摘要 | Studying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time-geographic-semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time-geographic-semantic (TGS) cube, based on which a TGS-weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix-projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID-19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID-19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were spindle-shaped, meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was wave-shaped, indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies. |
| WOS关键词 | RESILIENCE |
| WOS研究方向 | Geography |
| WOS记录号 | WOS:001134371300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201669]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Beijing Municipal Inst City Planning & Design, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Xuehua,Wang, Juanle,Zhang, Xiaodong,et al. Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial-temporal-semantic features[J]. TRANSACTIONS IN GIS,2024. |
| APA | Han, Xuehua,Wang, Juanle,Zhang, Xiaodong,Wang, Liang,&Xu, Dandan.(2024).Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial-temporal-semantic features.TRANSACTIONS IN GIS. |
| MLA | Han, Xuehua,et al."Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial-temporal-semantic features".TRANSACTIONS IN GIS (2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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