具有对比性的学习驱动的假新闻检测:保存语义,揭示扭曲
IEEE transactions on neural networks and learning systems
|December 22, 2025
概括
这项研究引入了一种新的对比学习驱动虚假新闻检测 (CLFD) 框架. CLFD有效地检测假新闻,仅使用文本,克服了社交网络中的数据稀缺性和语义完整性问题.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 信息科学 信息科学 信息科学
背景情况:
- 假新闻的检测受到数据稀缺和语义完整性损失的挑战.
- 现有的方法与现实世界的社交网络数据复杂性和增强限制作斗争.
研究的目的:
- 提出一个以学习驱动的对比性假新闻检测 (CLFD) 框架.
- 解决数据稀缺和语义完整性破坏现有方法的局限性.
- 开发一种只使用文本内容的通用和便携式假新闻检测方法.
主要方法:
- 开发了一个使用扭曲-逆转双视图操纵的CLFD框架.
- 采用可学习的神经网络来模拟非线性信息转换以生成对比视图.
- 实施了具有扭曲意识的对比学习和多目标联合优化策略.
主要成果:
- CLFD有效地产生多样化的观点,同时保持语义完整性,解决语义破坏问题.
- 该框架只使用文本内容实现了高效的检测,而不需要传播结构.
- 在基准数据集的准确性,稳定性和概括性方面表现出卓越的表现.
结论:
- 拟议的CLFD框架在假新闻检测方面取得了重大进展.
- 该方法具有很高的普遍性和可移植性,因为它仅依赖文本内容.
- CLFD提高了捕获欺骗性特征的能力,超过了最先进的方法.
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