Enhanced Multi-Class Detection of Fake News

Abstract

The spread of fake news has emerged as a critical challenge in the digital era. Confusion and conflict can arise if people mistake fake news for real news. Thus, advanced detection methodologies are desired. This paper aims to identify fake news, while addressing the issue of class imbalances. We employ multi-class fake news detection, an advanced methodology beyond traditional binary classification. We highlight CNN’s better performance over the baseline BERT model in the literature, with improvements in accuracy, precision, recall, and F1-Score. We uniquely experimented with four model variants: CNN and BERT with both trainable embeddings and BERT embeddings. Our experiment demonstrates CNN's effectiveness in identifying text patterns. To address class imbalances, we experimented with three different balancing methods. Our study includes fine-tuning ChatGPT for multi-class classification. The result indicates notable limitations in ChatGPT's automated classification, which highlights the complexities of AI-based categorization. Our findings demonstrate the CNN model's efficiency and effectiveness, and show the intricacies of fake news detection. These insights confirm the need for advanced AI methodologies in combating misleading information.