A Transfer learning-based model for fake news detection - ABIKOYE, Oluwakemi Christaina; ABDULSALAM, Taofeek Akintunde; AMINU, Enesi Femi; AWOTUNDE, Joseph Bamidele; ADENIYI, Abidemi Emmanuel (Edelweiss Applied Science and Technology, 2025)
The world we know today has vast information, events, data, and news spreading rapidly across the globe at exponential rates. The invention of modern telecommunication technology facilitates this exponential growth of fake news. With the exponential dissemination of news, fake news has also gained the potential to spread fast across social media and blog post spaces. Fake news is primarily aimed at spreading false information that is capable of influencing the opinion of the masses negatively. Previous researchers have proposed many techniques, including machine learning and deep learning, for fake news detection. Extensive studies show that past researchers have not yet established satisfactory performance, while some suffer from model overfitting on training sets with poor performance on testing data. Achieving optimum performance using a machine learning approach is highly difficult, especially when a statistical approach is adopted for the Natural Language Processing task. Hence, this study proposed a Transfer Learning-based Bidirectional Long Short-Term Memory to Machine Learning (Bi-LSTM-2-ML) model for the detection of fake news. The Bi-LSTM architecture is pre-trained on the phoney news dataset for extracting highly contextualized feature embedding, which is further used to enhance the training process of various machine learning algorithms, including SVM, LR, NB, DT, and KNN. The experimental results show that the Bi-LSTM-2-ML fake news detection model yielded better performance than previous approaches, and the Bi-LSTM-2-ML model enhanced the performance of machine learning algorithms with an 8.54% average increase in accuracy. In conclusion, the Bi-LSTM-2-ML model has demonstrated better performance by enhancing machine learning algorithms for detecting fake news.