Fake News Detection Using Feature Extraction, Natural Language Processing, Curriculum Learning, and Deep Learning

Following the advancement of the internet, social media gradually replaced the traditional media; consequently, the overwhelming and ever-growing process of fake news generation and propagation has now become a widespread concern. It is undoubtedly necessary to detect such news; however, there are certain challenges such as events, verification and datasets, and reference datasets related to this area face various issues such as the lack of sufficient information about news samples, the absence of subject diversity, etc. To mitigate these issues, this paper proposes a two-phase model using natural language processing and machine learning algorithms. In the first phase, two new structural features, along with other key features are extracted from news samples. In the second phase, a hybrid method based on curriculum strategy, consisting of statistical data, and a k-nearest neighbor algorithm is introduced to improve the performance of deep learning models. The obtained results indicated the higher performance of the proposed model in detecting fake news, compared to benchmark models.