SSM: Stylometric and semantic similarity oriented multimodal fake news detection

• Published in: Journal of King Saud University. Computer and information sciences Vol. 35; no. 5; p. 101559
• Main Authors: Nadeem, Muhammad Imran, Ahmed, Kanwal, Zheng, Zhiyun, Li, Dun, Assam, Muhammad, Ghadi, Yazeed Yasin, Alghamedy, Fatemah H., Eldin, Elsayed Tag
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2023; Elsevier
• Subjects: Deep learning; Fake news detection; Mulltimodal; Natural language processing; Semantic features; Stylometric features; Deep learning; Fake news detection; Semantic features; Natural language processing; Mulltimodal; Stylometric features
• ISSN: 1319-1578; 2213-1248
• DOI: 10.1016/j.jksuci.2023.101559

Over the years, there has been a rise in the number of fabricated and fake news stories that utilize both textual and visual information formats. This coincides with the increased likelihood that users will acquire their news from websites and social media platforms. While there has been various research into the detection of fake news in text using machine learning techniques, less attention has been paid to the problem of multimedia data fabrication. In this paper, we propose a Stylometric, and Semantic similarity oriented for Multimodal Fake News Detection (SSM). There are five distinct modules that make up our methodology: Firstly, we used a Hyperbolic Hierarchical Attention Network (Hype-HAN) for extracting stylometric textual features. Secondly, we generated the news content summary and computed the similarity between Headline and summary. Thirdly, semantic similarity is computed between visual and textual features. Fourthly, images are analyzed for forgery. Lastly, the extracted features are fused for final classification. We have tested SSM framework on three standard fake news datasets. The results indicated that our suggested model has outperformed the base line and state-of-the-art methods and is more likely to detect fake news in complex environments.