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@article{183130,
        author = {Mr.Sandip Niranjan Vende and Dr.Anubhav Kumar Prasad},
        title = {Social Media as a Public Health Sensor: Machine Learning for Early Detection and Response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {413-420},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183130},
        abstract = {Social media platforms generate vast amounts of data that can be leveraged for public health surveillance, sentiment analysis, and disease prediction. This study employs machine learning (ML) and natural language processing (NLP) techniques to analyze social media data for public health insights. Using datasets from Twitter, Reddit, and Facebook, we apply sentiment analysis, topic modeling, and predictive modeling to track health trends, misinformation, and public sentiment during health crises. A review of 20 Scopus-indexed papers highlights gaps in real-time analysis and bias mitigation. Our results demonstrate that AI-driven approaches improve early outbreak detection and public health response efficiency. The study contributes to the growing field of digital epidemiology and suggests policy implications for health monitoring systems.},
        keywords = {Artificial Intelligence, Social Media Analytics, Public Health, Machine Learning, Sentiment Analysis},
        month = {July},
        }