双流图增强型变压器模型集成BERT和GNN用于上下文意识的假新闻检测和假新闻检测
Hejamadi Rama Moorthy1, N J Avinash2, N S Krishnaraj Rao3
1Nitte (Deemed to be University), Nitte Institute of Professional Education (NIPE), Department of Computer Applications, Mangalore, Karnataka, India.
Scientific reports
|July 14, 2025
概括
这项研究介绍了一种新的双流图增强变压器模型,用于先进的假新闻检测. 通过将文本分析与基于图形的传播模式集成,该模型在识别在线错误信息方面实现了卓越的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 错误信息的传播需要先进的检测方法.
- 传统的假新闻检测通常忽略了新闻传播结构.
- 可信度评估需要整合语言和网络特征.
研究的目的:
- 提出一个双流图增强型变压器模型,用于增强假新闻检测.
- 为了利用BERT进行文字表示和图形神经网络 (GNN) 进行传播结构分析.
- 通过结合语言和基于网络的功能来改善假新闻检测.
主要方法:
- 使用BERT进行深入的文本理解.
- 使用图形注意网络 (GAT) 和图形变压器来建模传播结构.
- 集成的文本和图形嵌入使用基于注意力的融合机制进行分类.
主要成果:
- 在FakeNewsNet数据集上实现了99%的准确性.
- 性能优于基线模型,如Bi-LSTM和RoBERTa-GCN.
- 在使用组合功能检测假新闻方面表现出卓越的性能.
结论:
- 整合基于图形的传播功能显著提高了假新闻的检测.
- 拟议的模型提供了一个强大的,可扩展的,并具有上下文意识的解决方案.
- 未来的工作包括多语言和多模式的错误信息检测.
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