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Pattern Recognition in Social Media for Predicting Public Health Trends and Emergencies

Author(s) Ravikanth Konda
Country United States
Abstract The emergence of social media sites has brought about a dynamic setting where enormous amounts of real-time, user-generated information are generated every minute. This creates a one-of-a-kind chance to make use of digital content for public health surveillance, particularly in forecasting upcoming health trends and reacting to emergencies. This article examines the use of pattern recognition methods in social media analysis to detect early warning signs of public health occurrences. We specialize in using machine learning, natural language processing (NLP), and deep learning methodologies to process text, image, and network information from social media platforms such as Twitter, Facebook, Reddit, and Instagram. The objective is to develop prediction models that will be able to predict disease outbreaks, track mental health trends, and identify health misinformation. The framework proposed employs sentiment analysis, topic modeling, spatio-temporal data mining, and graph-based approaches to examine trends in social media data that are multilingual and multimodal. Real-world scenarios such as COVID-19, influenza surveillance, and mental health crisis identification are tested to determine model performance. Findings indicate that social media analysis can drastically lower response times and resource deployment in public health campaigns. The paper concludes with considerations for policy, ethics, and future research implications of this technology, with a focus on the need for interdisciplinary cooperation and data privacy concerns.
Field Engineering
Published In Volume 6, Issue 1, January 2025
Published On 2025-01-04
Cite This Pattern Recognition in Social Media for Predicting Public Health Trends and Emergencies - Ravikanth Konda - IJLRP Volume 6, Issue 1, January 2025. DOI 10.70528/IJLRP.v6.i1.1546
DOI https://doi.org/10.70528/IJLRP.v6.i1.1546
Short DOI https://doi.org/g9hndq
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