Semantic similarity and mutual information-based model for fake news detection

As internet use in communication networks has grown, fake news has become a big problem. The misleading heading of the news loses the trust of the reader. Many techniques have emerged, but they fail because fraudsters or exploiters find new ways to deceive them. Semantic analysis and machine learning techniques play a significant role in fake news detection. We must semantically assess terms used in the headline and main content before filtration because words can have different meanings in different contexts. In the paper, a method for determining fake news is introduced by calculating the dissimilarity between the title and content of the news. Vector distance calculators are used to extract semantic dissimilarities, which were then utilized as an additional feature. Initially, Term frequency-inverse document frequency (Tf-Idf) and Mutual Information (MI) are employed for text Feature Extraction (FE) on the ‘title’ and ’content’ of the news articles. Subsequently, four different vector distance calculators are used to extract vector distance-based features. The resulting distance values are used to train various machine learning classifiers, achieving the highest accuracy of 99%. Our method provides a comprehensive analysis by capturing diverse aspects of semantic dissimilarity from various distance calculators. The proposed method is then compared with previous techniques to demonstrate its effectiveness.