Fake news detection using enhanced features through text to image transformation with customized models

With the large use of social media, the dissemination of intentionally altered and falsified information has become easy, thus posing negative effects on society. Detecting fake content is a non-trivial task as fake news has unique characteristics and challenges. Additionally, the wide use of artificial intelligence (AI) for fake content generation makes the detection of fake content further complicated. Fake news presents engineered content, making it difficult for traditional approaches to comprehend. Existing fake news detection approaches face four problems: lack of robustness, adaptability, limited or no use of auxiliary information, and inability to handle diversity. Fake content diversity introduces the models’ complexities and degrades their performance. Similarly, the accuracy of fake news detection approaches remains low for practical systems. This study focuses on detecting fake news by using an AI-based approach to obtain high accuracy and robustness by using the concept of text transformation into images. It transforms the text into a standard image format which enriches the feature space and boosts the performance of machine learning models. Extensive experiments using two different datasets involving binary and multi-class classification reveal that the proposed approach outperforms existing solutions by yielding superior accuracy. The use of AI approaches helps obtain higher accuracy of 99.70% and 92% for fake news detection using ISOT and LIAR datasets, respectively.