The field of online social networks is moving towards a deeper understanding of the complex interactions between users, recommender systems, and the spread of information. Researchers are developing new models and algorithms to study the dynamics of opinion formation and the propagation of misinformation. A key area of focus is the design of recommendation algorithms that can mitigate the spread of fake content and promote more diverse and representative information. Another important direction is the development of diffusion models for influence maximization on temporal networks, which can help identify the most influential nodes and optimize the spread of information. Noteworthy papers include: Modelling the Closed Loop Dynamics Between a Social Media Recommender System and Users' Opinions, which proposes a mathematical model to study the coupled dynamics of a recommender system and user opinions. Agent-Based Exploration of Recommendation Systems in Misinformation Propagation, which uses agent-based modeling to examine the impact of various recommendation algorithms on the propagation of misinformation. Diffusion Models for Influence Maximization on Temporal Networks: A Guide to Make the Best Choice, which provides a structured guide to selecting the most suitable diffusion model for influence maximization on temporal networks.