一个国会推特网络数据集,量化对对影响的概率
Christian G Fink1, Nathan Omodt2, Sydney Zinnecker1
1Gonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.
Data in brief
|September 13, 2023
概括
本研究介绍了第117届美国国会的社交网络数据集,详细介绍了基于Twitter互动的成员之间的影响概率. 这个网络有助于理解政治社交网络中的信息传播.
科学领域:
- 社交网络分析 社交网络分析
- 政治科学 政治科学是指政治学.
- 计算社会科学 计算社会科学
背景情况:
- 了解政治沟通动态对于分析立法行为至关重要.
- 像Twitter这样的社交媒体平台已经成为政治话语和互动的重要道.
研究的目的:
- 创建第117届美国国会的新型社交网络数据集.
- 根据他们的Twitter活动来量化国会成员之间的"影响力概率".
主要方法:
- 从Twitter API V2收集了117届国会成员的交互数据 (转发,引用推文,回复,提及).
- 构建了一个有针对性的加权网络,边缘权重代表经验推导的影响概率.
- 通过每个国会议员发布的推特数量来规范化影响力指标.
主要成果:
- 在国会成员中生成了一组对对"影响力概率"的全面数据集.
- 该网络根据特定的Twitter互动捕捉了影响的定向性质.
结论:
- 开发的数据集为研究政治背景下的信息传播和网络结构提供了一个独特的资源.
- 这个网络可以促进对信息流动和立法者之间影响力动态的研究.
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