News dissemination prediction based on meta heuristic optimization fuzzy algorithm

The rapid growth of online platforms and social media has significantly changed how news is disseminated, creating challenges in predicting its reach and impact due to dynamic user behavior and unstructured information flow. This paper presents a novel approach for predicting news dissemination using a Meta Heuristic Optimization with Fuzzy Algorithm (MOFA). MOFA integrates Adaptive Particle Swarm Optimization (PSO) with fuzzy logic to effectively model the uncertain and evolving nature of news dissemination. By optimizing feature selection and parameter tuning, MOFA improves prediction accuracy and reduces the effects of noisy and inconsistent data. Experimental results demonstrate that MOFA outperforms traditional models, achieving a 15% improvement in prediction accuracy and a 12% reduction in error rates compared to existing state-of-the-art methods. The system is also scalable, handling large datasets from real-world social media platforms efficiently. The proposed method contributes significantly to real-time content monitoring, targeted advertising, fake news detection, and strategic communication in fields such as marketing, politics, and crisis management. Overall, MOFA offers a robust, adaptive solution to the complex challenges of predicting news dissemination, highlighting the potential of combining optimization techniques with fuzzy algorithms to address dynamic, data-driven problems.