Classification and Detection of Rumors Related to COVID-19 Using Machine Learning-Based Smart Techniques - Yancheng Yang; Junqiao Zhai; Shah Nazir (SAGE Open)
The COVID-19 coronavirus pandemic, a serious health risk, has affected information-related behavior and led to an upsurge in rumor-sharing on social media. Thus, combating COVID-19 necessitates combating rumors as well, which serves as a compelling incentive to examine rumor-related behavior during this unusual period. The analysis of the prior literature was summarized in the current study. For this, a number of well-known libraries were searched, including ScienceDirect, Springer, ACM, and IEEE Explore. The proposed research is based on a detailed overview of the detection and recognition of different deceptive news about the COVID-19 pandemic using various ML algorithms. It was found that with the implementation of the proposed approach, it is efficient to perform the classification of information into real and fake news on social media platforms. After studying different information detection techniques, various features have been identified from the literature. Then, important features extracted from the literature were used in the process of ranking. For the effective categorization of the available alternatives, the Graph Theory Matrix Approach is used. The alternatives are ranked based on their permanent function values. The current study has considered providing a comprehensive overview of the data that is currently available. The study demonstrates many methods for analyzing literature, enabling students to create fresh perspectives on the topic.