Enhancing Fake News Detection Using GPT-2 with a Hybrid Deep Learning Approach

Nowadays, the spread of fake news represents a growing problem. To overcome this issue, it is essential to develop effective fake news detection systems. In this paper, we augment the process of fake news detection using GPT-2 alongside Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM) networks. We use a dataset from Kaggle comprising of real and fake news articles. We compare the performance of the existing state-of-the-art real and fake news detection algorithms with our proposed hybrid model in Table 1. We blend GPT-2, known for contextual understanding, with either CNN or LSTM networks to capture more syntactical and semantic features from news articles and outperform the baseline algorithms. The preliminary results show that our hybrid model outperforms all the baseline algorithms on detecting real from fake news. By mixing generative pre-trained transformers with traditionally deep learning models, the robustness of misinformation detection systems can be significantly enhanced.