Multiplicative Vector Fusion Model for Detecting Deepfake News in Social Media

Abstract

In the digital age, social media platforms are becoming vital tools for generating and detecting deepfake news due to the rapid dissemination of information. Unfortunately, today, fake news is being developed at an accelerating rate that can cause substantial problems, such as early detection of fake news, a lack of labelled data available for training, and identifying fake news instances that still need to be discovered. Identifying false news requires an in-depth understanding of authors, entities, and the connections between words in a long text. Unfortunately, many deep learning (DL) techniques have proven ineffective with lengthy texts to address these issues. This paper proposes a TL-MVF model based on transfer learning for detecting and generating deepfake news in social media. To generate the sentences, the T5, or Text-to-Text Transfer Transformer model, was employed for data cleaning and feature extraction. In the next step, we designed an optimal hyperparameter RoBERTa model for effectively detecting fake and real news. Finally, we propose a multiplicative vector fusion model for classifying fake news from real news efficiently. A real-time and benchmarked dataset was used to test and validate the proposed TL-MVF model. For the TL-MVF model, F-score, accuracy, precision, recall, and AUC were performance evaluation measures. As a result, the proposed TL-MVF performed better than existing benchmarks.