Verma Pawan Kumar, Agrawal Prateek, Madaan Vishu, Prodan Radu
Present Address: Lovely Professional University, Phagwara, India.
MIT Art, Design and Technology University, Pune, India.
J Ambient Intell Humaniz Comput. 2022 Jul 27:1-13. doi: 10.1007/s12652-022-04338-2.
Online social media enables low cost, easy access, rapid propagation, and easy communication of information, including spreading low-quality fake news. Fake news has become a huge threat to every sector in society, and resulting in decrements in the trust quotient for media and leading the audience into bewilderment. In this paper, we proposed a new framework called essage ibility (MCred) for fake news detection that utilizes the benefits of local and global text semantics. This framework is the fusion of Bidirectional Encoder Representations from Transformers (BERT) using the relationship between words in sentences for global text semantics, and Convolutional Neural Networks (CNN) using N-gram features for local text semantics. We demonstrate through experimental results a popular Kaggle dataset that MCred improves the accuracy over a state-of-the-art model by 1.10% thanks to its combination of local and global text semantics.
在线社交媒体使信息能够低成本、便捷获取、快速传播且易于交流，其中包括传播低质量的虚假新闻。虚假新闻已成为对社会各领域的巨大威胁，导致媒体信任度下降，并使受众陷入困惑。在本文中，我们提出了一种名为消息可信度（MCred）的用于虚假新闻检测的新框架，该框架利用了局部和全局文本语义的优势。此框架是将用于全局文本语义的基于句子中单词关系的双向编码器表征（BERT）与用于局部文本语义的使用N元语法特征的卷积神经网络（CNN）相融合。我们通过实验结果表明，在一个流行的Kaggle数据集上，由于MCred结合了局部和全局文本语义，其准确率比最先进的模型提高了1.10%。
