The current research landscape in social media analysis is witnessing a significant shift towards leveraging advanced machine learning techniques, particularly Large Language Models (LLMs), to better understand and simulate complex social dynamics. A notable trend is the development of scalable and generalizable simulation platforms capable of modeling large-scale social interactions, which are crucial for replicating real-world phenomena on platforms like X and Reddit. These platforms are not only enhancing our ability to study group behaviors and polarization but also enabling the quantification of social consensus, a previously qualitative concept. Additionally, there is a growing focus on evaluating the capabilities of LLMs in understanding and generating content that aligns with social dynamics, particularly in contexts involving toxic interactions and minority stress. This includes the use of hybrid models combining Graph Neural Networks and Transformer-based architectures to detect nuanced expressions of stress in LGBTQ+ communities. Overall, the field is progressing towards more sophisticated, scalable, and context-aware models that promise to offer deeper insights into human behavior and social structures on digital platforms.