The field of social media analysis is moving towards the development of novel methods for integrating and analyzing multi-source data to improve situational awareness and crisis response. Researchers are leveraging AI-driven approaches, such as Latent Dirichlet Allocation and Large Language Models, to refine crisis-related social media content and identify subtle cognitive inconsistencies in malicious linguistic steganography. Another key direction is the study of decentralized social media protocols, with a focus on understanding how these protocols operationalize decentralization and distribute power among component owners. Noteworthy papers include: Signals from the Floods, which introduced a novel AI-driven method for refining crisis-related social media content. GSDFuse, which presented a state-of-the-art method for capturing cognitive inconsistencies in social media steganalysis. Bridging the Narrative Divide, which developed a platform-agnostic framework for reconstructing social graphs and information diffusion networks. Seeing the Politics of Decentralized Social Media Protocols, which analyzed four decentralized social media protocols to develop a novel conceptual framework for understanding how protocols operationalize decentralization.