Social Media Influence Maximization Using Multi-Deep Neural Networks

many people's daily lives revolve on online social networks. Millions of people have congregated digitally via social media. Studying social networks is difficult due to the huge number of individuals and data flows. On social media (SM), it is natural to see some people as more influential than others. Influence Maximization (IM) is the process of properly identifying influential users in order to maximize a particular piece of information throughout a network. In this paper proposed Multi-Deep Neural Networks to enhance the impacts. Multi-Deep Neural Network used Enhanced D-CNN (Deep Convolutional Neural Network) and DenseNet with BiCNN (Bi-directional Convolutional Neural Network) algorithms to investigate the largest user effect on Facebook, Instagram, and YouTube. Accuracy, precision, recall, and F-measure scores indicate the fastest increasing influencers on Facebook, Instagram, and YouTube. The result shows Instagram had the largest user growth between 2023 and 2024.