估计社交网络上的情感两极分化
Marilena Hohmann1, Michele Coscia2
1Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark.
PloS one
|September 24, 2025
概括
研究人员开发了一种新的基于网络的情感两极分化的测量方法,在线评估敌意和社会距离. 这种方法揭示了随着时间的推移,Twitter上的COVID-19讨论中日益增加的两极分化.
科学领域:
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
- 沟通研究 沟通研究
背景情况:
- 人们普遍担心社交媒体平台上的两极分化和仇恨言论.
- 情感两极分化,以党派敌意为特征,是将政治分歧与在线敌对语言联系起来的关键因素.
- 现有的在线情感两极分化测量方法有限,不考虑敌意和社会距离.
研究的目的:
- 解决在线测量情感两极化的方法差距.
- 提出一种基于网络的新措施,将敌意和社会距离结合在一起.
- 为了能够独立研究两极化的情感和社会距离组成部分.
主要方法:
- 开发一种基于网络的情感两极分化措施.
- 共同分析敌意 (情感组成部分) 和社会距离 (交互模式).
- 该措施应用于关于COVID-19讨论的大规模Twitter数据集.
主要成果:
- 拟议的措施准确地反映了分歧水平和表达的敌意之间的关系.
- 它还捕捉了社会距离的组成部分,表明了互动和避免的选择.
- 对COVID-19推特数据的分析显示,在2020年2月,情感两极分化较低,在随后的几个月显著增加.
结论:
- 这项新的基于网络的措施有效地通过结合敌意和社会距离来量化在线情感两极分化.
- 随着用户参与度的增加,Twitter上关于COVID-19讨论的情感两极分化升级.
- 这种方法提供了一种更全面的方法来理解在线党派敌意.
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