Social media networks significantly shape public opinion through dynamic interactions. To better capture this process, we propose a novel opinion dynamics model integrating asynchronous and synchronous updates, reflecting real-world social media behaviors. Each user holds both private and public opinions private opinions evolve continuously based on public discourse and personal biases, while public opinions update only upon expression. Our model incorporates heterogeneous interaction strengths, echo chamber effects, and topic-driven engagement, where the probability of opinion expression is influenced by trending topics and viral content. This framework enables a comprehensive analysis of opinion convergence, polarization, and fragmentation, highlighting the role of network structure and user engagement in shaping discourse. We derive conditions under which opinions stabilize, diverge, or form clusters, revealing distinct pathways to consensus or ideological division. To validate our approach, we compare simulated opinion trajectories with real-world Twitter data, demonstrating its ability to replicate observed discourse patterns. These insights provide a deeper understanding of opinion evolution in digital spaces and offer strategies to mitigate misinformation and foster healthier online discussions. Furthermore, the model demonstrates computational feasibility for analyzing large-scale networks, supporting its applicability in real-world scenarios.