Semantic difference-based feature extraction technique for fake news detection

The rise of fake news presents a critical challenge to societal stability, emphasizing the urgent need for efficient detection systems. This study introduces an innovative approach to identifying fake news by exploiting the semantic discrepancies between the titles and content of news articles. Our method involves first summarizing article contents using a feature selection (FS) technique, followed by calculating vector distances between the titles and summarized contents using Cosine similarity, Jaccard distance and Euclidean distance. These methods enable us to identify multiple semantic dissimilarity characteristics, resulting in a more thorough examination. The obtained distance values are combined and used as features in our fake news detection models. These composite features are used to train various machine learning (ML) and deep learning (DL) models across three distinct news article datasets. Our approach achieves an accuracy rate of nearly 99.9%. The results underscore the effectiveness of leveraging semantic differences between article titles and content, offering a robust alternative to methods that focus solely on individual textual components. This technique not only improves accuracy but also provides a scalable solution to combating fake news in digital media.