A novel self-learning semi-supervised deep learning network to detect fake news on social media

Xin Li, Peixin Lu, Lianting Hu, XiaoGuang Wang, Long Lu
2021 Multimedia tools and applications  
Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides false information, which has a significant negative impact on society. Especially recently, the false information about the new coronavirus disease 2019 (COVID-19) has spread like a virus around the world. The state of the Internet is forcing the world's tech giants to take unprecedented action to protect the
more » ... on health" of the public. Despite many existing fake news datasets, comprehensive and effective algorithms for detecting fake news have become one of the major obstacles. In order to address this issue, we designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases, thus improving the accuracy of the neural network. Experimental results indicate that our network is more accurate than the existing mainstream machine learning methods and deep learning methods.
doi:10.1007/s11042-021-11065-x pmid:34093070 pmcid:PMC8170457 fatcat:ccfrshrlmzdgtehxxikhtvkrhe