EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Muhammad Imran Nadeem, Kanwal Ahmed, Dun Li (), Zhiyun Zheng, Hend Khalid Alkahtani, Samih M. Mostafa, Orken Mamyrbayev () and Hala Abdel Hameed
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Muhammad Imran Nadeem: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Kanwal Ahmed: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Dun Li: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Zhiyun Zheng: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Hend Khalid Alkahtani: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Samih M. Mostafa: Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
Orken Mamyrbayev: Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan
Hala Abdel Hameed: Faculty of Computer and Information Systems, Fayoum University, Fayoum 63514, Egypt

Sustainability, 2022, vol. 15, issue 1, 1-24

Abstract: Due to the exponential increase in internet and social media users, fake news travels rapidly, and no one is immune to its adverse effects. Various machine learning approaches have evaluated text and images to categorize false news over time, but they lack a comprehensive representation of relevant features. This paper presents an automated method for detecting fake news to counteract the spread of disinformation. The proposed multimodal EFND integrates contextual, social context, and visual data from news articles and social media to build a multimodal feature vector with a high level of information density. Using a multimodal factorized bilinear pooling, the gathered features are fused to improve their correlation and offer a more accurate shared representation. Finally, a Multilayer Perceptron is implemented over the shared representation for the classification of fake news. EFND is evaluated using a group of standard fake news datasets known as “FakeNewsNet”. EFND has outperformed the baseline and state-of-the-art machine learning and deep learning models. Furthermore, the results of ablation studies have demonstrated the efficacy of the proposed framework. For the PolitiFact and GossipCop datasets, the EFND has achieved an accuracy of 0.988% and 0.990%, respectively.

Keywords: multimodal fake news detection; Multimodal Factorized Bilinear pooling; natural language processing; social sensing; misinformation/disinformation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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