双MGCL:谣言检测通过双向多层次图形对比学习.
Weiwei Feng1, Yafang Li2, Bo Li1
1School of Computer Science and Engineering, Beihang University, Beijing, Beijing, China.
PeerJ. Computer science
|December 11, 2023
概括
这项研究介绍了BiMGCL,这是一种用于使用双向图对比学习检测在线谣言的新框架. BiMGCL通过有效建模传播结构和提高对各种谣言事件的强度来提高谣言检测准确度.
科学领域:
- 人工智能的人工智能
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 大型语言模型加速谣言的产生,挑战社交媒体内容的真实性.
- 目前用于传闻检测的深度学习方法缺乏稳定性,并且无法充分利用结构信息.
研究的目的:
- 提出一个新的谣言检测框架,BiMGCL,解决当前方法的局限性.
- 提高谣言识别和检测的准确性和稳定性.
主要方法:
- 模拟谣言传播结构作为细粒度的双向图.
- 在节点和图表层面采用自我监督的对比学习.
- 使用三种可解释的双向图形数据增强策略.
主要成果:
- 与最先进的方法相比,BiMGCL表现出优越的谣言检测性能.
- 该框架通过双向图形建模和对比学习有效地捕获谣言传播特征.
结论:
- BiMGCL提供了一种强大而有效的解决方案,用于在社交媒体上检测谣言.
- 拟议的框架通过整合结构信息和高级学习技术来推动该领域的发展.
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