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International Journal of Advanced Research in Computer and Communication Engineering
International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
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← Back to VOLUME 15, ISSUE 6, JUNE 2026

CreatorPulse: An AI-Driven Multi-Platform Content Creation, Viral Prediction, and Automated Social Media Publishing System

Sanket Dhage, Sanmati Ukhalkar, Vedant Ghare, Krushna Nikam, Prof. R. P. Daund

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Abstract: The rapid growth of the creator economy has placed enormous pressure on individuals and agencies to produce high-quality, platform-tailored social media content consistently and at scale. Despite the existence of scheduling tools and AI writing assistants, no single platform currently integrates intelligent content generation, viral potential scoring, AI image creation, and automated cross-platform publishing within one cohesive system. This paper presents CreatorPulse, a full-stack Software-as-a-Service (SaaS) platform that addresses these gaps using a dual microservices architecture β€” a Node.js/Express REST API and a Python FastAPI AI engine β€” alongside a React.js dashboard. The system integrates OpenAI GPT-4o and Anthropic Claude Sonnet for generating seven distinct content formats tailored to platform-specific rules and the creator's personal voice. A novel Hybrid Viral Prediction Engine (HVPE) scores every piece of content on a 0–100 scale using a weighted blend of rule-based heuristics and LLM evaluations across five dimensions: hook quality, optimal length, hashtag usage, engagement potential, and trend alignment. The platform further incorporates DALL-E 3 for generating platform-specific thumbnails and carousel images, a Celery-based ETA scheduler for automated publishing to Twitter/X, LinkedIn, Instagram, and Facebook, a NewsAPI-driven trend detection engine, and a closed-loop performance feedback system that retrains the scoring model weekly from real engagement data. Experimental evaluation on 500 generated posts across five platforms demonstrates a mean viral prediction accuracy of 82.4%, average content generation latency of 3.24 seconds, and a 67% reduction in content creation time compared to manual workflows.

Keywords: Artificial Intelligence; Large Language Models; Social Media Automation; Viral Prediction; SaaS; DALL- E 3; Prompt Engineering; Celery Task Queue; Content Scheduling; Multi-Platform Publishing

How to Cite:

[1] Sanket Dhage, Sanmati Ukhalkar, Vedant Ghare, Krushna Nikam, Prof. R. P. Daund, β€œCreatorPulse: An AI-Driven Multi-Platform Content Creation, Viral Prediction, and Automated Social Media Publishing System,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15695

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