The recent developments in the field of fake news detection and sentiment analysis have shown significant advancements, particularly in leveraging machine learning and natural language processing techniques. Researchers are increasingly focusing on creating more sophisticated models that can accurately distinguish between credible and non-credible news, with a growing emphasis on adapting these models to specific topics and contexts. The integration of epidemiological knowledge into rumor detection models has also shown promise, enhancing the robustness and adaptability of these systems. Additionally, the use of large language models for generating stance labels in rumor detection has streamlined the process, reducing the reliance on costly human annotation. In sentiment analysis, the creation of domain-specific lexicons, such as the Economic Lexicon, has improved the accuracy and relevance of sentiment measures in economic contexts. These innovations collectively push the boundaries of current capabilities in detecting and mitigating the spread of misinformation, offering more reliable tools for maintaining information integrity in various domains.