Empirical Study of Semantic Analysis to Generate True Time Authenticity Scores for Subreddits Microblogging

Fake news is now widely disseminated through social media networking platforms. These stories have factual inaccuracies and crumbed material, and as a result of these inaccuracies, unfavorable events have arisen in society. Various verification methods and algorithms have been used on social media platforms to manage such content. In a prior study, the linguistic structures of text were employed to detect false or authentic contents, and machine learning and deep learning algorithms were used to discern temporal patterns. The complex models of neural network architecture have been employed in recent research to aid in the selection of the right parameters from the dataset as well as the calculation of superior outcomes. In order to identify whether certain articles or statements are true or fake, this study employed two separate datasets: one to train the model and the other to generate real-time authenticity scores. The deep neural networks’ LSTM and Bi-LSTM techniques were used to train the suggested model. This research was successfully tested using real-time subreddit blogs, generating authenticity measures with their probability scores and verifying the blogs’ trustworthy sources.