The field of social network dynamics and opinion formation is witnessing significant advancements, with a growing focus on understanding the complex interactions between individuals and groups in online platforms. Researchers are developing new models and frameworks to capture the underlying mechanisms of social network formation, including meritocracy-based and Matthew-effect-based models. These efforts aim to explain the distribution of social power and the evolution of content ecosystems on online platforms. Furthermore, the study of opinion dynamics is becoming increasingly important, with applications in politics, marketing, and pandemic mitigation. Innovative approaches, such as the use of reinforcement learning and large language models, are being explored to disrupt social networks and amplify social dissensus. Noteworthy papers in this area include: Disrupting Networks: Amplifying Social Dissensus via Opinion Perturbation and Large Language Models, which introduces a reinforcement learning framework to fine-tune large language models for generating disruption-oriented text. When Small Acts Scale: Ethical Thresholds in Network Diffusion, which presents a minimal message-passing model to study the diffusion of actions in networked environments and identifies a threshold separating subcritical, critical, and supercritical regimes.