A Comprehensive Scrutiny on Fake News Detection Techniques

Social media has recently taken over as the main source for individuals to learn about what is occurring in the world. Every day, fake news appears on social media. Social media fake news has damaged a number of industries, including politics, the economy, and health. Furthermore, it has had a negative impact on society's stability. Although various research have provided valuable models for recognizing fake news in social networks using a variety of methodologies, there are still certain restrictions and difficulties. Data augmentation, feature extraction, and data fusion are some of the approaches explored in this review to improve detection accuracy. Moreover, it discusses the most prominent techniques used in detection models and their main advantages and disadvantages. This review aims to help other researchers improve fake news detection models. In this review, many techniques to increase detection accuracy were investigated, including data augmentation, feature extraction, and data fusion. Additionally, it addresses the most popular strategies employed in detection models as well as their primary benefits and drawbacks. This review is intended to assist other academics in developing false news detecting algorithms.