Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2674-2682.DOI: 10.11772/j.issn.1001-9081.2023091359

• Artificial intelligence • Previous Articles     Next Articles

Multi-domain fake news detection model enhanced by APK-CNN and Transformer

Jinjin LI, Guoming SANG(), Yijia ZHANG   

  1. Information Science and Technology College,Dalian Maritime University,Dalian Liaoning 116026,China
  • Received:2023-10-09 Revised:2023-12-08 Accepted:2023-12-11 Online:2024-03-21 Published:2024-09-10
  • Contact: Guoming SANG
  • About author:LI Jinjin, born in 2000, M. S. candidate. Her research interests include natural language processing, rumor detection.
    ZHANG Yijia, born in 1979, Ph. D., professor. His research interests include natural language processing, social media computing.
  • Supported by:
    National Natural Science Foundation of China(62072070);Fundamental Research Funds for Central University(3132019207)

APK-CNN和Transformer增强的多域虚假新闻检测模型

李金金, 桑国明(), 张益嘉   

  1. 大连海事大学 信息科学技术学院,辽宁 大连 116026
  • 通讯作者: 桑国明
  • 作者简介:李金金(2000—),女,河南漯河人,硕士研究生,CCF会员,主要研究方向:自然语言处理、谣言检测
    桑国明(1971—),男,辽宁大连人,副教授,硕士,主要研究方向:自然语言处理、人工智能
    张益嘉(1979—),男,辽宁大连人,教授,博士,主要研究方向:自然语言处理、社会媒体计算。
  • 基金资助:
    国家自然科学基金资助项目(62072070);中央高校基本科研业务费项目(3132019207)

Abstract:

In order to solve the problems of domain shifting and incomplete domain labeling in social media news, as well as to explore more efficient multi-domain news feature extraction and fusion networks, a multi-domain fake news detection model based on enhancement by APK-CNN (Adaptive Pooling Kernel Convolutional Neural Network) and Transformer was proposed, namely Transm3. Firstly, a three-channel network was designed for feature extraction and representation of semantic, emotional, and stylistic information of the text and view combination of these features using a multi-granularity cross-domain interactor. Secondly, the news domain labels were refined by optimized soft-shared memory networking and domain adapters. Then, Transformer was combined with a multi-granularity cross-domain interactor to dynamically and weighty aggregate the interaction features of different domains. Finally, the fused features were fed into the classifier for true/false news discrimination. Experimental results show that compared with M3FEND (Memory-guided Multi-view Multi-domain FakE News Detection) and EANN (Event Adversarial Neural Networks for multi-modal fake news detection), Transm3 improves the comprehensive F1 value by 3.68% and 6.46% on Chinese dataset, and 6.75% and 11.93% on English dataset; and the F1 values on sub-domains are also significantly improved. The effectiveness of Transm3 for multi-domain fake news detection is fully validated.

Key words: fake news detection, domain shift, soft-shared memory networking, Transformer, APK-CNN (Adaptive Pooling Kernel Convolutional Neural Network)

摘要:

为解决社交媒体新闻中的领域转移、领域标签不完整问题,以及探索更高效的多域新闻文本特征提取和融合网络,提出一种基于APK-CNN(Adaptive Pooling Kernel Convolutional Neural Network)和Transformer增强的多域虚假新闻检测模型Transm3。首先,设计三通道网络对文本的语义、情感和风格信息进行特征提取和表示,并利用多粒度跨域交互器对这些特征进行视图组合;其次,通过优化的软共享内存网络和域适配器来完善新闻领域标签;再次,将Transformer与多粒度跨域交互器结合,使用更先进的融合网络动态加权聚合不同领域的交互特征;最后,将融合特征输入分类器中用于真/假新闻判别。实验结果表明,Transm3与M3FEND(Memory-guided Multi-view Multi-domain FakE News Detection)和EANN(Event Adversarial Neural Networks for multi-modal fake news detection)相比,综合F1值在中文数据集上分别提高了3.68%和6.46%,在英文数据集上分别提高了6.75%和11.93%,在各分领域上F1值也有明显的提高,充分验证了Transm3在多域虚假新闻检测工作上的有效性。

关键词: 虚假新闻检测, 领域转移, 软共享内存网络, Transformer, APK-CNN

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