AENeT: an attention-enabled neural architecture for fake news detection using contextual features

• Published in: Neural computing & applications Vol. 34; no. 1; pp. 771 - 782
• Main Authors: Jain, Vidit, Kaliyar, Rohit Kumar, Goswami, Anurag, Narang, Pratik, Sharma, Yashvardhan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer architecture; Computer Science; Data Mining and Knowledge Discovery; Datasets; Deep learning; Digital media; Embedding; Image Processing and Computer Vision; Metadata; Neural networks; News; Original; Original Article; Probability and Statistics in Computer Science; Search engines; Semantics; Smartphones; Social networks; Social research; Deep learning; Contextualized Features; Neural Network; Fake News; Social Media
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06450-4

In the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news article is genuine or fake is not easy for any end user. Search engines like Google are also not capable of telling about the fakeness of any news article due to its restriction with limited query keywords. In this paper, our end goal is to design an efficient deep learning model to detect the degree of fakeness in a news statement. We propose a simple network architecture that combines the use of contextual embedding as word embedding and uses attention mechanisms with relevant metadata available. The efficacy and efficiency of our models are demonstrated on several real-world datasets. Our model achieved 46.36% accuracy on the LIAR dataset, which outperforms the current state of the art by 1.49%.