通过纵向社交媒体分析量化环境意识的时间动态
Marija Stojcheva1, Jana Prodanova2, Aleksandra Dedinec1,2
1Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boskovikj 16, 1000 Skopje, Macedonia.
Chaos (Woodbury, N.Y.)
|February 13, 2026
概括
公众对空气污染的认识随着污染水平的波动而波动,随着时间的推移显示出适应性转变. 这项研究使用社交媒体数据和复杂系统分析量化了公众感知的动态.
科学领域:
- 环境科学 环境科学
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
背景情况:
- 社会对环境变化的看法是一个动态的过程.
- 了解公众意识需要分析人与环境的相互作用.
- 社交媒体提供了丰富的公共话语纵向数据来源.
研究的目的:
- 分析公众对空气污染意识的时间演变.
- 使用社交媒体话语量化集体感知动态.
- 研究环境条件与公众意识之间的关系.
主要方法:
- 对三年来社交媒体上有关空气污染的数据进行了纵向分析.
- 使用Valence Aware字典和情感推理器 (VADER) 和Twitter-RoBERTa进行情感分析.
- 使用BERTopic进行主题建模,以确定主导话语主题.
- 情绪指标与PM10水平之间的相关性和相互信息分析.
- 使用克鲁斯卡尔-瓦利斯测试来评估时间变化的统计分析.
主要成果:
- 在公众情绪指标和PM10水平之间观察到同步波动.
- 公共意识显示出越来越多的依赖或塑造的污染变化.
- 情绪分布的统计学上显著的时间变化证实了公众意识的适应性转变.
- 随着时间的推移,确定与空气污染相关的主导话语主题.
结论:
- 公众对环境问题的认识是社会环境系统中可测量的顺序参数.
- 集体感知源于环境强迫和公共信息流之间的非线性反.
- 一个多层次的社会环境感知框架可以从社交媒体数据中量化新出现的意识动态.
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