Understanding Vaccine Hesitancy: A Machine Learning Approach to Analyzing Social Media Discourse
Abstract
Vaccine hesitancy poses a significant challenge to public health efforts worldwide, impacting immunization rates and the control of infectious diseases. Understanding the underlying reasons and dynamics behind vaccine hesitancy is crucial for designing effective interventions and communication strategies. In this study, we propose a novel approach leveraging machine learning techniques to analyze social media discourse related to vaccine hesitancy. By collecting and analyzing a large volume of social media data, we aim to identify key themes, sentiments, and influential factors contributing to vaccine hesitancy. Through advanced natural language processing (NLP) algorithms, we seek to uncover patterns, trends, and correlations in user-generated content, providing insights into the public perception and attitudes towards vaccination. The findings of this research can inform public health authorities, policymakers, and healthcare practitioners in developing targeted interventions to address vaccine hesitancy and promote vaccine acceptance.
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