通过随机矩阵理论来检测Twitter上的论领袖的结构性方法
Saeedeh Mohammadi1,2, Parham Moradi1,2, Andrey Trufanov3
1Physics Department, Shahid Beheshti University, Tehran, 1983969411, Iran.
Scientific reports
|December 8, 2023
概括
随机矩阵理论 (RMT) 揭示了社交媒体网络中的精英用户,而不仅仅是受欢迎的用户. 这种方法揭示了在线讨论中隐藏的影响力,特别是在选举期间.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 政治沟通的政治沟通
背景情况:
- 社交媒体话语网络是复杂的,用户的影响力通常被认为遵循简单的受欢迎度量.
- 了解这些网络中的影响力和权力结构的真实动态对于分析在线通信至关重要.
研究的目的:
- 引入和应用随机矩阵理论 (RMT) 来识别社交媒体话语中的有影响力的用户.
- 分析2021年伊朗总统大选的转发网络中的权力动态.
- 探索RMT在发现更深层次的网络结构和讨论动态方面的潜力.
主要方法:
- 应用随机矩阵理论 (RMT) 来分析社交媒体转发网络.
- 补充RMT的方法,以全面了解网络结构和动态.
- 专注于2021年伊朗总统大选的转发数据.
主要成果:
- 在RMT分析中,除了简单的"一对多"模式之外,还发现了有影响力的用户.
- 一个精选的用户群体在他们的集群和整体网络中表现出显著的影响.
- 该研究揭示了通过传统网络分析无法立即发现的复杂的动力动态.
结论:
- 随机矩阵理论 (RMT) 是一个强大的工具,用于发现社交媒体中隐藏的影响和网络动态.
- 这些发现为用户影响提供了新的视角,超越了基本的人气指标.
- 这种方法在研究各种在线对话中的意见领袖方面具有广泛的适用性,政治或其他.
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