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.
Copyright © 2022 Fei Wu and Xiaoyu Luo.