AI for predicting human behaviors in social media

In the digital age, social networking platforms have transformed into complex ecosystems that reflect human behaviors and interactions. This work leverages Artificial Intelligence (AI) to analyze, model, and predict individual and group behaviors using the vast digital footprints left on these platforms. By integrating Natural Language Processing (NLP), Sentiment Analysis, Deep Learning, Social Network Analysis (SNA), and Graph Neural Networks (GNNs), the study proposes a comprehensive framework for understanding online dynamics. This research introduces Evolution-GPT, a novel transformer-based model designed to capture temporal changes in user behavior rather than relying on static snapshots. Unlike traditional approaches, Evolution-GPT predicts how user moods, community affiliations, and perspectives evolve over time, offering dynamic, context-aware insights. Additionally, GNNs are utilized to identify hidden patterns, influence networks, and the spread of information or misinformation across online communities. Privacy, fairness, and transparency are embedded in the system's design to ensure ethical AI deployment and maintain user trust. Potential applications include marketing (adapting to changing customer sentiments), psychiatry (early detection of anxiety or depression through linguistic shifts), and public policy (tracking echo chambers and misinformation trends). This work bridges deep learning, network science, and ethical AI practices to provide a new paradigm for modeling and predicting evolving online behaviors.