Data Mining Techniques in Social Media
Raja Rajeswari Sethuraman · Apple Academic Press eBooks · 2025
Social media has become a crucial tool in the digital era for raising public awareness of healthcare monitoring. It allows people to share information about healthcare policy and practice issues, develop professional networks, and educate themselves about patient— caregiver interactions. Physicians and pharmacists use social media to read news articles, discuss patients’ issues, and conduct research for medical improvements. Web-based data has emerged, contributing to infectious disease surveillance, reducing costs, and providing real-time statistics. Social media platforms like Facebook, Twitter, YouTube, Linkedln, and Instagram can be used to prevent diseases, provide moral support, manage medication, and treat diseases. However, dealing with the vast amount of real-time data can be overwhelming. Data mining methods, such as statistical models, machine learning, models, decision trees, and neural networks, can help streamline the process and enhance decision-making. These methods are integral in various healthcare applications, including analysis, planning, diagnosis, integration, prevention, learning, therapy development, problem-solving, and decision-making. Techniques such as association, clustering, prediction, classification, regression, and sequential patterns can be used to analyze healthcare data in social media.