在虚假新闻级联中跟踪不断发展的社区,使用时间图表.
Yanfei Ma1, Daozheng Qu2, Yibo Wang3
1Department of Computer Science, Fairleigh Dickinson University, Vancouver, V6B 2P6, Canada.
Scientific reports
|January 9, 2026
概括
我们开发了TIDE-MARK,以随着时间的推移跟踪社交媒体上的假新闻社区. 这种方法有效地识别出传播错误信息的稳定,相互联系的社区,其表现优于现有技术.
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
- 信息科学 信息科学 信息科学
背景情况:
- 错误信息在社交媒体平台上迅速传播,用户群体充满活力,社区结构不断变化.
- 现有的方法往往忽视时间动态或使用静态聚类,未能在信息级联中捕捉不断变化的社区行为.
- 在信息布中对社区的纵向跟踪因其持续的发展,合并或解体而复杂化.
研究的目的:
- 提出TIDE-MARK,一种用于识别虚假新闻级流中的社区的新方法,以保持结构和时间一致性.
- 在动态的社交网络中为一致和可解释的社区轨迹提供统一的框架.
- 通过使用真实世界的假新闻数据集,评估TIDE-MARK与强大的基线的有效性.
主要方法:
- 通过时间图神经网络使用节点嵌入来表示网络结构和动态.
- 使用原型驱动的集群和马尔科夫建模来进行社区检测和过渡分析.
- 实施基于增强的改进,以提高社区识别的准确性和稳定性.
主要成果:
- 在结构性 (模块化,导电性) 和时间性 (调整后的兰德指数) 两个指标上,TIDE-MARK表现优于基线.
- 分析显示,假新闻通过更稳定,相互联系的社区传播,与通过分散的社区传播的真实新闻不同.
- 模拟表明,针对新兴社区的结构意识干预可以显著减少错误信息的传播和级联模块化.
结论:
- 潮标志为实时假新闻监测提供了一个强大的,结构意识的框架,优先考虑网络动态而不是内容分析.
- 该方法为复杂的社会系统中创新的动态社区监测提供了基础.
- 泰德-马克的可解释架构支持道德应用和开发内容中立的缓解错误信息策略.
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