Fake News Detection Through Topic Modeling and Optimized Deep Learning with Multi-Domain Knowledge Sources

Abstract

Increased internet access has exacerbated the severity of fake news on social media leading to employing advanced deep learning methods using large-scale data. Most of these methods rely on supervised models, demanding a large volume of training data to avoid overfitting. This paper presents Fake news Identification using Bidirectional Encoder Representations from the Transformers (BERT) model with optimal Neurons and Domain knowledge (FIND), a two-step automatic fake news detection model. To accurately detect it, the FIND approach applies a deep transformer model such as the BERT with the large-scale unlabeled text corpus to facilitate the classification model, and the Latent Dirichlet allocation (LDA) topic detection model to examine the influence of the article’s headline and the body individually and collaboratively. The proposed FIND approach outperforms the existing exBAKE approach in terms of 10.78% of the greater F-score.