一个多层次的网络方法来揭示在线政治选择性曝光的模式
Yuan Zhang1, Laia Castro2, Frank Esser1
1Department of Communication and Media Research, University of Zurich, Zurich, Switzerland.
PloS one
|September 22, 2025
概括
对政治信息的选择性接触比简单的左右分歧要复杂得多. 一项新的多层次分析揭示了基于网络解决方案的等级影响者社区和多样化的受访者参与.
科学领域:
- 计算社会科学 计算社会科学
- 政治沟通的政治沟通
- 网络分析 网络分析
背景情况:
- 选择性曝光,倾向于支持确认信息,驱动着极化和回声室.
- 传统的政治分析往往简化了选择性暴露在左-右意识形态尺度上,忽视了细微差别.
- 了解复杂的选择性暴露模式对于理解在线政治话语至关重要.
研究的目的:
- 引入和测试一个多层次的分析框架,使用多规模的社区检测来研究选择性暴露.
- 调查政治影响者网络的等级结构和受访者参与模式.
- 探索不同级别的社区解决方案如何揭示网络指数和个人属性之间的不同关联.
主要方法:
- 结合了2022年巴西总统大选的调查和Twitter/X数据.
- 建立了一个两党网络,将调查受访者与政治影响者联系起来.
- 应用多级社区检测到预期的影响者网络,以揭示层次集群.
主要成果:
- 确定了政治影响者的等级集群,有不同的受访者子组共同关注特定的集群.
- 证明选择性暴露特征与分析的社区解决水平有显著差异.
- 发现在更精细的分辨率下,社区重叠与意识形态,人口统计和新闻习惯相关;在更粗的分辨率下,只有意识形态是重要的.
结论:
- 在线政治选择性曝光比一维的左右频谱所暗示的要复杂得多.
- 多层次的方法对于准确地捕捉选择性暴露模式的复杂性至关重要.
- 以不同分辨率的基于网络的分析提供了更深入的见解,了解个人如何在线接触政治信息.
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