A Hybrid Transformer-Based Model for Optimizing Fake News Detection

Fake news, colloquially referred to as false news, exerts a profound influence on fundamental facets of our societal framework. Manual fact verification is a frequently utilized strategy for mitigating the harmful impacts of false news transmission. However, when assessing the massive volume of freshly produced material, manual fact verification is insufficient. Furthermore, the quantity of labeled datasets is limited, people are unreliable annotators, resources are largely in the English language, and they mostly concentrate on articles related to news. Cutting-edge deep learning algorithms are employed to deal with this challenge automatically and address these difficulties. Nevertheless, the large number of models and variability of characteristics utilized in the literature frequently constitute a barrier for researchers attempting to enhance the effectiveness of models. This paper introduces a model designed to address the issue of false news, with a focus on analyzing news headlines. The approach is rooted in a hybrid classification model where our model integrates a BERT architecture, and the outputs are seamlessly linked to bi-directional deep learning layers, including a bi-LSTM layer and a bi-GRU layer. The model's training and evaluation were conducted using the WELFake dataset, comprising four prevalent news databases. A comparative analysis was carried out using the proposed model and standard classification models. Additionally, standard machine learning and deep learning models along with vanilla BERT were trained on the same dataset with comparable constraints to assess the impact of integrating a bi-directional deep learning layer with BERT. The results revealed an improvement in accuracy, as our proposed model achieved an accuracy of 98.1% and an F1 score of 0.982.