Multiplicative Vector Fusion Model for Detecting Deepfake News in Social Media

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 fak...

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Bibliographic Details
Published in:Applied sciences 2023-04, Vol.13 (7), p.4207
Main Authors: Salini, Yalamanchili, Harikiran, Jonnadula
Format: Article
Language:English
Subjects:
Access to information
Analysis
Benchmarks
Classification
Computational linguistics
Datasets
Deception
Deep learning
detection
Digital media
Disinformation
fake news
False information
Information dissemination
Internet
Language processing
Literature reviews
Model accuracy
National security
Natural language interfaces
Natural language processing
Sentences
Social media
Social networks
TL-MVF
Transfer learning
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13074207