Behavioural Analysis in Web Pattern Mining of Social Media Networks Using Deep DenseNet Classification

Web pattern mining in social media networks has gained significant attention due to the abundance of user-generated content. Understanding user behaviors and preferences within these networks is crucial for personalized recommendations, targeted marketing, and content optimization. In this study, we propose a novel approach for behavioral analysis in web pattern mining of social media networks using deep DenseNet classification. We formulate the task as a multi-class classification problem, where each class corresponds to a specific user behavior pattern. Our proposed approach leverages the expressive power of DenseNet, a deep neural network architecture, to automatically learn intricate features from raw web data, capturing both local and global patterns. We present a comprehensive experimental evaluation on a real-world social media dataset, demonstrating the effectiveness of our approach in accurately classifying diverse user behaviors. The results highlight the superiority of deep DenseNet classification over traditional methods, showcasing its potential for enhancing behavioral analysis in the context of web pattern mining.