Predicting the Veracity of News Articles Using Multimodal Embeddings and NLP-Based Features

The prevalence of false news in today's culture has brought to light the necessity for automated systems that can recognize and categorize news stories according to their validity with accuracy. The validity of news items is predicted using multimodal embeddings and characteristics based on natural language processing (NLP) in this work. In this method, the textual and visual content of news articles are jointly modeled using multimodal embeddings, and NLP-based features like sentiment, readability, and subjectivity are extracted. A machine learning model is then trained using these features to categorize news articles as true or false. The performance of the suggested method has been assessed against a number of cutting-edge algorithms for the identification of false news using a huge dataset of news items. The experimental findings demonstrate the efficacy of multimodal embeddings and NLP-based features by showing that it produces greater accuracy and F1 scores in comparison to other techniques. The suggested method offers a potential technique for the automatic identification and categorization of news stories based on their truthfulness, which might have significant effects on the fight against the spread of false information in modern society.