Lurker:基于后门攻击的可解释的谣言检测在在线媒体上
Yao Lin1, Wenhui Xue1, Congrui Bai1
1School of Information Science and Technology, Northwest University, Xi'an, China.
Science progress
|January 7, 2025
概括
这项研究介绍了Lurker,一个新的算法,通过识别它们独特的传播结构来检测在线谣言. 露克有效地发现关键用户及其互动,提高了谣言检测的准确性.
科学领域:
- 图形神经网络 图形神经网络
- 计算社会科学 计算社会科学
- 网络分析 网络分析
背景情况:
- 图形神经网络 (GNN) 在图形级别的分类方面表现出色,包括谣言检测.
- 在线谣言通过操纵网络 (例如机器人) 迅速传播,表现出明显的早期传播结构.
- 识别这些结构是区分谣言和可信信息的关键.
研究的目的:
- 开发一个可解释的谣言检测算法,灵感来自后门攻击.
- 识别关键的在线用户及其互动模式,从而定义谣言传播结构.
- 将谣言检测转化为检测特定传播结构的问题.
主要方法:
- 使用因果分析来识别因果子图,确定图形分类 (谣言与正常).
- 提取关键在线用户并探索他们的特定传播模式.
- 实施基于后门的方法,将已识别的特殊传播结构植入检测模型中作为触发器.
主要成果:
- 提出的算法,Lurker,在检测特殊的谣言传播结构方面表现出有效性.
- 在三个真实世界数据集上的实验结果验证了算法的性能.
- 与基线方法相比,Lurker取得了显著的改进,攻击成功率增加了高达33.1%,清洁精度下降了61.8%.
结论:
- 这项研究成功地提出了一个可解释的,以后门为灵感的谣言检测算法.
- 该方法有效地识别了关键用户和传播结构,这对于谣言识别至关重要.
- 卢克提供了一种有前途的方法来提高谣言检测系统的准确性和可解释性.
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