利用BERTopic-SKEP综合模型对多阶段应急管理政策进行公众反分析
Cui Li1,2, Qiyu Tian3, Lei Gao1,2
1School of Economics and Management, Institute of Disaster Prevention, Sanhe, 065201, China.
Scientific reports
|December 1, 2025
概括
本研究量化了公众对应急管理政策的反,使用了新的深度学习框架. 它揭示了各个政策阶段的重大关注差异和情绪转变,为灾害治理提供了洞察力.
科学领域:
- 灾害管理和治理的管理和治理.
- 计算社会科学 计算社会科学
- 人工智能在公共政策中的作用
背景情况:
- 有效的灾害治理依赖于理解公众在多阶段应急管理政策中的反.
- 之前的方法在简短的社交媒体数据中分析公众情绪和担忧时遇到了困难.
研究的目的:
- 开发和应用一个集成的深度学习框架,以量化公众对"2.3.7"北京暴雨期间政府行动的看法.
- 分析公众的担忧和情绪反应在四个紧急管理阶段:预防,准备,响应和恢复.
主要方法:
- 使用BERTopic进行主题建模,使用SKEP进行情绪分析,共使用50 015个社交媒体帖子.
- 集成的语义嵌入和结构化的情感知识,以克服传统主题建模的局限性.
- 应用了叙述性政策框架 (NPF) 来解释发现作为结构化的政策叙述.
主要成果:
- 公众的注意力主要集中在应对和恢复阶段 (超过76%),对预防和准备工作的关注很少 (低于2.5%).
- 恢复阶段呈现出高的积极性 (78.18%),表明公众对灾后行动的认可.
- 在NPF分析中,公众认为政府是政策叙述中的"英雄"或"规划者".
结论:
- 综合的BERTopic-SKEP框架有效地捕捉了紧急管理中的细微的公众需求和情感动态.
- 调查结果为优化灾害治理的政策合法性和运营效率提供了可操作的见解.
- 了解多阶段的公众反对于加强灾难响应和恢复工作至关重要.
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