Hunan Institute of Engineering, 411104 Xiangtan, Hunan, China.
Hunan University of Technology and Business, 410205 Changsha, Hunan, China.
Comput Intell Neurosci. 2022 Apr 28;2022:1493493. doi: 10.1155/2022/1493493. eCollection 2022.
Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.
假新闻在全球范围内迅速传播，被认为是现代科技最严重的问题之一，需要立即解决。社交媒体作为重要信息来源的使用显著增加，加上对传统媒体的信任动摇、数字新闻传播速度快以及互联网上大量信息的传播，使得所谓的假新闻问题更加严重。本工作通过利用用户之间的友谊所产生的信息来证明检测假新闻的重要性。具体来说，我们使用创新的深度时间卷积网络（DTCN）方案，辅助使用张量分解非负 RESCAL 方法，在分解过程中和分解过程中利用有类别意识的速率表，生成更准确的表示，以极高的可靠性检测假新闻。通过这种方式，展示了开发自动检测虚假信息方法的必要性，主要目的是保护读者免受错误信息的影响。
