基于图形的分层集成网络用于在社交媒体上的文本新闻文章中检测宣传
Pir Noman Ahmad1,2, Jiequn Guo3, Nagwa M AboElenein4
1Ningbo China Institute for Supply Chain Innovation, MIT Global SCALE Network, Ningbo, 315832, China. pirnoman@hit.edu.cn.
Scientific reports
|January 13, 2025
概括
一个新的基于层次图的集成网络 (H-GIN) 能够有效地检测文本中的宣传. 该模型达到82%的准确性,在识别错误信息和假新闻方面超过现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 信息科学 信息科学 信息科学
背景情况:
- 在COVID-19期间,社交媒体平台成为传播信息和错误信息的关键.
- 图形神经网络 (GNN) 在NLP中表现有前途,但由于复杂的数据,在宣传检测方面遇到了困难.
- 挑战包括挖掘单词交互和处理广泛的,非连续的上下文数据.
研究的目的:
- 引入一种新的基于层次图的集成网络 (H-GIN),用于基于文本的宣传检测.
- 解决现有的GNN模型在捕捉错误信息分析的复杂文本关系方面的局限性.
- 开发一个多标签分类模型,以加强宣传识别.
主要方法:
- 拟议的H-GIN模型具有双层间-内通道图形结构.
- 集成的剩余驱动增强和处理 (RDEP) 用于远程节点交互.
- 利用了注意力驱动的多通道功能融合 (ADMF) 来标准化三通道 (序列,语义,语法) 层.
主要成果:
- 在ProText,Qprop和PTC数据集中检测宣传的准确率达到了82%.
- 与当前的领先型号相比,表现出卓越的性能.
- 在ProText数据集上成功识别了以前未见的例子,准确度为82%.
结论:
- H-GIN提供了一个强大的解决方案,用于在文本数据中检测宣传.
- 该模型的层次图形结构和特征融合增强了它分析复杂信息的能力.
- 在网络上,H-GIN显示出了打击虚假新闻和宣传传播的巨大潜力.
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