Navigating user engagement and cultural transitions in entertainment technology and social media based on activity management

Abstract

Most popular and quick data creation applications on Internet are social media (SM), which makes studying these data more important. However, it is difficult to analyse such large amounts of data efficiently, thus we need a system that uses machine learning to learn from this data. Systems can learn on their own thanks to machine learning techniques. Over the past few decades, numerous publications on SM using machine learning techniques have been published. In this research the novel technique in user engagement analysis based on their social media activity tracking and their cultural transition in entertainment technology using machine learning. Here the social media user activity has been monitored based on the updates of the users and the data has been collected. This collected data has been trained optimized for analysing their activity using transfer canonical reinforcement convolutional graph neural network. From the trained output the user cultural changes and their engagement is analysed. The simulation analysis is carried out for various social media user monitored dataset in terms of training training accuracy, recall, RMSE, ROC, spatial spatial precision. Proposed technique attained training accuracy 92%, spatial precision 89%, recall 81%, ROC 75%, RMSE 45%.