一个传闻检测模型,包含传播路径,上下文语义和用户信息.
Lin Bai1, Xueming Han1, Caiyan Jia1
1School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044 China.
概括
本研究介绍了DAN-Tree和DAN-Tree++用于社交媒体上的谣言检测. 这些模型有效地识别谣言传播的关键特征,并整合用户配置文件,以获得卓越的早期检测性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 社交媒体分析 社交媒体分析
背景情况:
- 发现谣言对于减轻社交媒体上传播的错误信息至关重要.
- 现有的方法往往忽略了传播模式中的关键结构和用户特定特征.
- 这导致在识别和标记虚假信息方面表现不佳.
研究的目的:
- 开发先进的模型,以更准确,更有效地检测谣言.
- 通过整合结构和用户信息来解决当前方法的局限性.
- 为了提高在线谣言的早期检测能力.
主要方法:
- 提出了一个在传播树结构 (DAN-Tree) 上的双重注意力网络,利用一个节点和路径双重注意力机制.
- 实现了路径超采样和结构嵌入,以增强深度传播结构的学习.
- 通过将用户配置文件集成到传播树中来提高性能,引入了DAN-Tree ++.
主要成果:
- 与最先进的模型相比,DAN-Tree在基于传播结构的四个谣言数据集上表现出更高的性能.
- 在两个数据集上结合用户配置文件和传播结构时,DAN-Tree++显著优于其他模型.
- 无论是DAN-Tree还是DAN-Tree++都在早期谣言检测任务中取得了最高的性能.
结论:
- 拟议的DAN-Tree和DAN-Tree++模型在谣言检测方面取得了重大进展.
- 整合结构和用户特定特征对于提高模型准确性和早期检测至关重要.
- 这些模型为打击在线虚假信息的传播提供了一个强大的框架.
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