基于临时树转换器在社交网络上检测谣言
Sirong Wu1,2,3, Yuhui Deng1,2,3, Junjie Liu4
1Guangdong Provincial Key Laboratoryof Interdisciplinary Research and Application for Data Science, Beijing NormalUniversity-Hong Kong Baptist University United International College, Zhuhai,Guangdong Province, China.
PloS one
|April 7, 2025
概括
检测社交媒体谣言至关重要. 我们的时间树转换器模型分析文本,结构和时间变化,以准确检测谣言,优于现有方法.
科学领域:
- 社交计算社会计算
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 社交媒体促进了谣言的快速传播,导致社会问题.
- 自动传闻检测是必不可少的,但目前的方法缺乏动态时间分析.
- 现有的方法往往忽略了谣言传播的不断变化的结构.
研究的目的:
- 提出一种新型模型,即临时树转换器,用于自动传闻检测.
- 将谣言传播的文字,结构和时间动态纳入其中.
- 提高谣言检测系统的概括性和准确性.
主要方法:
- 使用Gated Recurrent Unit (GRU) 来编码时间传播树结构.
- 在不同的时间窗口中分析了繁殖树的生长.
- 员工休假一次事件 (LOEO) 交叉验证,以进行现实的评估.
主要成果:
- 在PHEME数据集上实现了75.84%的最先进的精度.
- 获得了71.98%的宏观F1得分.
- 通过提取时间特征,证明了改进的模型概括.
结论:
- 时间树转换器有效地捕捉了谣言传播的动态特征.
- 整合时间信息显著提高了谣言检测性能.
- 拟议的方法为现实世界的谣言检测场景提供了更强大的方法.
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