The field of social media analysis and misinformation detection is rapidly evolving, with a growing focus on human-centric approaches and the integration of large language models (LLMs). Researchers are exploring new methods for detecting and mitigating the spread of misinformation, including the use of neuro-behavioural models and the analysis of social dynamics and emotional responses. The development of multimodal datasets and the application of LLMs to simulate online conversations and forecast internet traffic are also notable trends. Noteworthy papers include: The Psychology of Falsehood, which surveys the evolving interplay between traditional fact-checking approaches and psychological concepts, and Identifying Constructive Conflict in Online Discussions, which proposes a framework for surfacing constructive conflicts in online discussions. Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season is also noteworthy, as it conducts a comprehensive study of LLMs during the election season and releases a rich dataset for future evaluations.