Enhancing Social Media User Engagement Through Personalized Content Classification

Abstract

In the fast-evolving world of social media, user engagement is key to platform success. This study presents a novel approach to enhancing engagement through advanced classification algorithms for personalized content delivery, moving beyond generic strategies. The framework analyzes user behavior to provide tailored recommendations, adapting to changing interests and improving the overall experience. The classification algorithms effectively identify user preferences, resulting in more relevant content and higher interaction rates. The implementation and impact of these algorithms demonstrate that personalized engagement boosts content discoverability and strengthens user-platform relationships. Additionally, this article introduces a technology for classifying Facebook users using Particle Swarm Optimization (PSO). As social media evolves, this research aims to refine engagement strategies, highlighting the need for personalized content delivery to create a user-centric experience.