Fake News Detection Using Feature Extraction, Resampling Methods, and Deep Learning

The production of fake news were practiced even before the advent of the internet. However, with the development of the internet and traditional media giving way to social media, the growing and unstoppable process of making and spreading this kind of news have become a widespread concern. Fake news by disrupting the proper flow of information and deluding public opinion, potentially causes serious problems in society. Therefore, it is necessary to detect such news, which is associated with some challenges. These challenges may be related to various issues such as datasets, events, or audiences. Lack of sufficient information about news samples, or an imbalance are the main problems in some of these datasets, which will be addressed in this paper. In the proposed model, firstly the key features in relevant datasets will be extracted to increase information about news samples. After that, using the K-nearest neighbors, a genetic, and TomekLink algorithms as the cleaning techniques, as well as designing a Generative Adversarial network, as a technique for generating synthetic data, three novel methods in the area of hybrid resampling will be presented to balance these datasets. The presented methods cause a significant increase in the performance of the deep learning algorithms to detect fake news.