伪新闻检测的混合深度学习模型:阿拉伯语和英语语言的案例研究
Baqer M Merzah1, Jafar Razmara1, Zolfaghar Salmanian1
1Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, Iran.
Frontiers in big data
|January 22, 2026
概括
这项研究引入了一种深度学习模型,用于阿拉伯语和英语中检测假新闻,实现高精度. 该模型有效地打击社交网络上的错误信息.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 在线虚假新闻的扩散对公共话语构成重大威胁.
- 现有的假新闻检测模型往往缺乏多语言能力,并表现出较低的准确性,特别是对于阿拉伯语.
- 阿拉伯语的资源限制和语言复杂性为发现错误信息带来了独特的挑战.
研究的目的:
- 开发一种有效的深度学习技术,用于阿拉伯语和英语的假新闻检测 (FND).
- 解决单语言模型的局限性,提高阿拉伯语内容的检测精度.
主要方法:
- 提出了一种新的深度学习模型,集成多通道卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM).
- 该模型使用预训练的FastText模型来捕获语义和本地文本特征.
- 采用全球最大共享层来减少维度和提取特征,然后进行分类.
主要成果:
- 该模型在基准数据集上实现了高准确率:在AFND (阿拉伯语) 上达到94.43%,在ANS (阿拉伯语) 上达到71.63%,在WELFake (英语) 上达到98.85%.
- 实验结果表明该模型的优越性超过现有的最先进的 (SOTA) 方法.
- 拟议的技术在检测不同语言的假新闻方面表现出显著的有效性.
结论:
- 开发的深度学习模型为打击在线虚假信息提供了强大的解决方案.
- 这种方法在提高通过社交网络传播信息的可靠性方面具有实际应用.
- 这项研究有助于多语言假新闻的检测,特别是提高了阿拉伯语的表现.
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