The recent developments in the field of social media and content moderation are significantly advancing the capabilities of large language models (LLMs) and their applications. A notable trend is the use of LLMs to address issues of political polarization and hate speech on social media platforms. Innovations in re-ranking algorithms aim to mitigate affective polarization by controlling exposure to content that fosters antidemocratic attitudes and partisan animosity. Additionally, the optimization of LLMs for stance detection in vaccine-related misinformation highlights the potential for these models to enhance content annotation and moderation. However, the effectiveness of generative AI in generating counterspeech to combat online hate speech remains a contentious area, with studies suggesting that context-specific interventions may not always yield positive outcomes. Furthermore, the fragmentation of the social media ecosystem into ideologically homogeneous niches is being systematically analyzed, providing insights into the dynamics of platform specialization and user migration. Advances in content moderation technologies, particularly those leveraging LLMs, are showing promise in detecting and censoring sensitive content across various media formats, improving accuracy and reducing false positives and negatives. The integration of topic modeling and sentiment analysis in understanding public sentiment, particularly in contexts like nuclear energy discourse, underscores the evolving role of LLMs in social media research and public opinion analysis.