Padmavathy L.Radha NNithya S2025-03-152024-05-23978-981976683-3https://dspace.psgrkcw.com/handle/123456789/5402By disseminating fake information, fake news plays a significant part in influencing people’s knowledge and perceptions as well as their decision-making. By incorporating real news into fake news, online forums and social media have encouraged its dissemination. Fake news has become the primary obstacle to having a greater impact in the information-driven environment for determined fakers. Due to a number of features in the dataset, testing on a single dataset in the current system may produce false results. The performance of classifying fake news is reduced. This study introduces the Levy flight-based dove swarm optimization (LDSO) and deep neural network (DNN) algorithms for the detection of fake news. The preprocessing, news-user engagement matrix design, feature selection, and fake news classification method are the primary phases of this effort. Preprocessing is carried out using stemming, stop word removal, and tokenization on the features from BuzzFeed and PolitiFact datasets. It is used to eliminate extraneous features to increase the accuracy of predicting fake news. The news-user engagement matrix is then constructed for detection. Latent representation of together the news content and the social context is obtained with tensor by a coupled matrix-tensor factorization algorithm. These features are included in the process of feature selection, which is carried out by the LDSO algorithm. Then DNN method is used to classify fake news by dealing with a variety of filters across every dense layer by dropout. Based on the experimental findings, it was determined that the proposed LDSO-DNN algorithm outperforms the existing methods by increased precision, recall, F-measure, and accuracy.en-USOther