Enhancing the Fake News Classification Model Using Find-Tuning Approach

Abstract

In recent years, fake news on social media has become a pressing concern, posing significant threats to individuals, organizations, and society as a whole. we present a novel strategy to enhance the accuracy of fake news classification models through fine-tuning. Our proposed model involves adding new layers and freezing some layers in the BEART model, resulting in improved performance. To facilitate our research, we constructed a comprehensive fake news dataset by combining real and fake datasets obtained from secondary sources. Firstly, the dataset underwent rigorous pre-processing, including data cleaning, text normalization, tokenization, stop word removal, and other techniques, ultimately enabling binary classification. Subsequently, the proposal model (DCNN) was trained on this dataset to classify news articles as either real or fake. Notably, experimental results demonstrate that our approach outperforms several recent studies in detecting fake news, achieving high accuracy. To evaluate the effectiveness of our proposed model, we employed various evaluation methods. Firstly, we utilized the Tag Cloud technique, which visually represents the most frequently used words in the text or documents, enabling us to distinguish between real and fake news. Additionally, we employed the Classification report, which provides precision, F1, recall, and support scores, to comprehensively assess the model's performance. Furthermore, we employed the confusion matrix, a tabular layout that effectively visualizes the classification algorithm's performance, thereby enabling a clear interpretation based on known true values. Therefore, the proposed model was trained to classify news articles as real or fake, and the experiments on the dataset show that this approach performs better than several recent studies for detecting fake news, achieving high accuracy.