KG-MFEND:一种高效的基于知识图的模型,用于多域虚假新闻检测
Lifang Fu1, Huanxin Peng2, Shuai Liu2
1Northeast Agricultural University, Harbin, 150030 China.
概括
本研究介绍了KG-MFEND,这是一种用于使用知识图 (KG) 检测多域假新闻的新框架. 它有效地识别了各种主题的假新闻,以增强的概括能力优于现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 社交媒体上的假新闻带来了重大的社会风险.
- 现有的假新闻检测模型由于特定领域的语言变化而缺乏跨领域的适用性.
- 一个强大的,多域虚假新闻检测系统对于现实世界的社交媒体环境至关重要.
研究的目的:
- 为多域虚假新闻检测提出一个新的框架.
- 通过整合外部知识和解决领域差异,加强假新闻检测.
- 提高虚假新闻检测模型的概括能力.
主要方法:
- 开发了一个名为KG-MFEND的新框架,用于多域虚假新闻检测.
- 集成的外部知识使用一个构建的知识图 (KG) 与多域信息.
- 通过注入实体三倍来构建句子树来提高BERT模型性能.
- 利用软位置和可见矩阵用于知识嵌入和标签平滑以减轻噪音.
主要成果:
- 在单个,混合和多个领域中,KG-MFEND展示了强大的泛化能力.
- 拟议的模型在多域虚假新闻检测方面超过了当前最先进的方法.
- 在中国数据集上的实验结果验证了KG-MFEND框架的有效性.
结论:
- KG-MFEND框架为多域虚假新闻检测提供了一个强大的解决方案.
- 整合知识图和增强的BERT模型有效地解决了特定领域的挑战.
- 该研究强调了跨领域假新闻识别的实际重要性和有效性.
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