<p>Media websites must select news that is engaging for their audience out of the abundance of articles from the newsrooms. Even though the news was formerly only chosen by human editors, media outlets have begun implementing editorial algorithms due to an increase in the volume of news. Natural language processing (NLP) technology developments and the flood of social media data have entirely changed how editorial decisions are made in modern journalism. The paper proposes a Social-NLP Editorial Optimization Framework (SNLP-EOF) for integrating social media analytics with intelligent support enabled by natural language processing to optimize news editing judgments. Using social media platforms, editors can obtain significant insights into pertinent themes, public attitudes, and developing trends. By utilizing natural language processing (NLP) methods with advanced news editorial algorithms (ANEA), one can extract valuable information from unorganized textual data and identify important themes, attitudes, and opinions voiced by communities on the internet. By combining social media analytics with NLP-driven insights, editors can make well-informed judgments about content selection, prioritization, and dissemination. The proposed approach improves news coverage's relevance, timeliness, and engagement. Furthermore, editors can receive real-time feedback, automated content curation, and tailored recommendations from intelligent assistance systems with NLP capabilities. These features streamline the editorial workflow and increase productivity. To improve news editorial judgments, this study examines the theoretical underpinnings, methodological structures, and practical effects of integrating social media analytics with natural language processing techniques. The study also showcases the proposed approach's inspiring potential in shaping journalism's future.</p>