Stacking-Based Ensemble Learning on Low Dimensional Features for Fake News Detection

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. This has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches.