Deep learning for fake news detection: A comprehensive survey

Abstract

The information age enables people to obtain news online through various channels, yet in the meanwhile making false news spread at unprecedented speed. Fake news exerts detrimental effects for it impairs social stability and public trust, which calls for increasing demand for fake news detection (FND). As deep learning (DL) achieves tremendous success in various domains, it has also been leveraged in FND tasks and surpasses traditional machine learning based methods, yielding state-of-the-art performance. In this survey, we present a complete review and analysis of existing DL based FND methods that focus on various features such as news content, social context, and external knowledge. We review the methods under the lines of supervised, weakly supervised, and unsupervised methods. For each line, we systematically survey the representative methods utilizing different features. Then, we introduce several commonly used FND datasets and give a quantitative analysis of the performance of the DL based FND methods over these datasets. Finally, we analyze the remaining limitations of current approaches and highlight some promising future directions.