在2020年大选期间,在Twitter和Parler上识别和描述意识形态同质的集群
Daniel Verdear1, Ashley Hemm2, Zuoyu Tian3
1Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America.
PloS one
|December 10, 2025
概括
意识形态均的群体在社交媒体上更有效地传播选举错误信息. 右倾团体使用类似于阴谋论的语言,为#StopTheSteal运动做出了贡献.
科学领域:
- 分析社会网络分析.
- 计算社会科学 计算社会科学
- 政治沟通是政治沟通.
背景情况:
- 2020年美国大选周期通过公开声明和社交媒体,广泛怀疑选举合法性.
- 这些情绪在1月6日的国会大起义中达到顶峰.
- 了解像#StopTheSteal这样的运动的在线动态对于分析政治话语至关重要.
研究的目的:
- 分析网络层面和内容层面的数据,以了解在线#StopTheSteal运动的有效性.
- 在主流 (Twitter) 和替代 (Parler) 社交网络上识别和比较意识形态同质的群体.
- 研究政治话语区分的语言特征.
主要方法:
- 利用卢温集群来识别意识形态上同质的群体.
- 开发并应用了一种用于组分析的新型同质度指标.
- 分析了来自Twitter和Parler的讨论数据.
- 对不同组的文本特征进行内容分析.
主要成果:
- 意识形态上同质的群体被发现在传播信息方面比多元化的群体更有效.
- 倾向于右派的团体表现出与阴谋论文本的风格相似之处,特别是使用"世界建设"语言.
- 在左倾和右倾同质群体之间的文本特征中观察到显著的差异.
结论:
- 意识形态的一致性显著放大了在线政治讨论中的信息传播.
- #StopTheSteal运动中的右倾话语的语言风格与阴谋论中发现的模式保持一致.
- 网络和内容分析为与选举有关的错误信息和政治动员的传播提供了宝贵的见解.
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