An Adaptive Social Media Ecosystem Using IoT and Deep Learning for Personalized User Interaction and Well-Being Optimization

Abstract

Social media companies can now personalize user experiences by adapting to users’ emotional and cognitive states through IoT devices and advanced machine learning algorithms. This study develops a wearable IoT-based adaptive social media system that employs deep learning to enhance user interactions and overall well-being. Smartwatches and fitness trackers are used to detect heart rate, physical activity, and sleep patterns in real time, while mobile sensors track user interactions and application usage. Advanced deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are applied to collect and analyze these data to identify emotional patterns. The system estimates users’ cognitive load and emotional states to dynamically adapt content delivery and interaction strategies. When stress is detected, the system curates content to reduce anxiety and adjusts notification frequency to prevent cognitive overload. Sentiment analysis is applied to user-generated content to further refine content recommendations. A continuous feedback loop enhances model accuracy and system personalization based on user responses. Through these mechanisms, the proposed system aims to improve user satisfaction and mental well-being by delivering personalized social media experiences. This study contributes to the advancement of emotion-aware digital environments and demonstrates how IoT and machine learning technologies can enhance online user interactions.