Intelligent Recognition and Prediction of Social Media User Behavior Patterns in the Internet of Things Environment

Incorporating the Internet of Things in social media networks and lifestyle has created a new way of analyzing behavior in connected environments. This paper proposes a framework for real-time detection and forecasting of users’ behaviors based on a combination of IoT sensor data and social media posts. An advanced deep architecture approach was designed that integrates CNN, LSTM, attention mechanisms, and reinforcement learning to analyze the multiple layers of data and make accurate predictions regarding future behaviors. We evaluated the system using data collected from public APIs and IoT gadgets and achieved satisfactory results regarding the recognition rate ([Formula: see text]1-score of 89%) and generation time (63 ms per prediction). The identification of benchmarks and overrepresented features was confirmed by the clustering and visualization step, and the statistical significance of the identified features was supported by statistical analyses. A real-world case study of a smart building was given, which showed real-world functions such as anomaly detection as well as energy efficiency enhancement. We propose this research to comprehend how intelligent behavior can be monitored in a smart, user-centered environment in a scalable and adaptive manner.