The recent advancements in the field of media bias and misinformation detection have shown a significant shift towards integrating structural and temporal data with traditional text-based models. Researchers are increasingly adopting hybrid approaches that combine the strengths of Graph Neural Networks (GNNs) and Pre-trained Language Models (PLMs) to capture both textual and relational information. These hybrid models are proving to be more effective in handling complex data structures and improving the accuracy of predictions related to media bias, factuality, and misinformation spread. Additionally, there is a growing emphasis on developing scalable and real-time solutions that can adapt to dynamic social interactions and diverse propagation patterns. The integration of human and machine forecasts in geopolitical event prediction is also gaining traction, demonstrating improved accuracy and scalability. Notably, the field is witnessing innovative frameworks that leverage hierarchical temporal knowledge graphs to model rumor dynamics and provide actionable insights for misinformation control. These developments highlight the potential for proactive intervention strategies and underscore the importance of addressing limitations in coverage, speed, and reach of fact-checking efforts. Overall, the research direction is moving towards more comprehensive and adaptive models that can effectively combat misinformation and enhance decision-making processes.