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@article{194950,
        author = {Nandyala Sameeksha and B. Chandana and Miriyala Sruthi Gayathri},
        title = {Automated Social Media Analytics Tool},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6026-6034},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194950},
        abstract = {The rapid increase in social media platforms has led to the generation of huge amounts of user interaction data. Analysing this data may lead to useful insights for enhancing user engagement and optimizing content strategy. This paper introduces a Multi-Platform Social Media Analytics and Post Optimization System that utilizes analytics from different platforms such as Facebook, Instagram, YouTube, and GitHub. The suggested system utilizes APIs to collect social media data and machine learning algorithms to analyse user engagement patterns and optimize the timing of publishing content. A web-based user interface developed using Flask assists in visualizing analytics results and providing recommendations to optimize social media performance. The suggested system analyses historical user engagement data such as likes, comments, shares, and views to detect patterns in user interaction data. The experimental evaluation of the suggested system demonstrates the effectiveness of the system in analysing the social media data and optimizing the performance of the user.},
        keywords = {Social Media Analytics, Machine Learning, Flask, Instagram API, Facebook API, YouTube API, Post Optimization.},
        month = {March},
        }