利用内容制作者网络和用户感知来检测在线话语社区
Stefano Guarino1, Ayoub Mounim2, Guido Caldarelli3,4,5
1National Research Council of Italy, Institute for Applied Mathematics, Rome, Italy. stefano.guarino@cnr.it.
Scientific reports
|March 3, 2026
概括
本研究引入了一个新的框架,通过对有影响力的用户进行聚类,并将标签扩展到更广泛的用户群体来检测在线社区. 这种方法有效地识别了Twitter/X等社交媒体平台上的政党和联盟.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 在线社区检测在线社区检测
背景情况:
- 在线讨论表现出行为不对称性,很少有用户创建内容,许多人消费/重新分发它.
- 在大型在线社交网络中确定有意义的社区仍然是一个挑战.
研究的目的:
- 为在线社交网络提出一个社区检测框架,利用用户行为不对称.
- 识别和集群领先的用户,并将这些标签扩展到更广泛的用户群.
主要方法:
- 提出了一个社区检测框架,重点是首先聚合有影响力的用户.
- 介绍了两种领导者聚类策略:相互交互和受众重叠,使用基于的过.
- 该框架在Twitter/X上的意大利政治辩论中进行了评估,使用公众人物作为领导者.
主要成果:
- 与标准基线相比,拟议的框架产生了与政治结构 (政党和联盟) 保持一致的更加连贯和可解释的社区.
- 这两种集群变体成功地恢复了不同的政治实体.
- 基于活动的领导人选择产生了较弱的结果,特别是在联盟级别的识别方面.
结论:
- 公众认可的数字的统计验证网络为识别在线话语社区提供了坚实的基础.
- 该方法利用了许多在线平台共同的结构不对称性,并且在概念上是一般的.
- 该框架为了解在线社会动态和社区结构提供了一个强大的工具.
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