Enhancing Social Media User Engagement Through Personalized Content Classification
Suman Mann, Nitish Pathak, Musaed Alhussein, Jyoti Prajapati, Khursheed Aurangzeb, Neelam Sharma
Source record
Source: Crossref
Published: Feb 14, 2025
DOI: 10.37256/cm.6120255700
Open original source ↗Source 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.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.