Some Observations on Social Media Mining tools for Health Applications

Abstract

The use of social media has immensely increased globally, which accordingly have generated tremendous amounts of data that have in turn attracted many researchers. Accessing, analyzing and mining this Big Social Data bring about a great deal of challenges. Social Media has ample data for various applications including the health domain that we have seen recently during the outbreak of COVID-19. Various applications and frameworks were designed for real-time monitoring and detection of disease spread, patient’s health status, providing information about active cases, hotspot areas, vaccination centers and also for disseminating government advisories. Social Media Mining provides tools and techniques to mine Social Media for extracting information, study patterns and analyze data. Social Media Mining can be used to bridge the gap between current technologies and healthcare systems. This paper basically reviewed various tools, techniques and algorithms developed for Social Media Mining for Health Applications (SMM4HA) and also discussed the major challenges that emanate using SMM4HA such as the issue of health misinformation and rumors, privacy and security issues, issue of data breaches, Big Social Data and the ethical challenges. We have also discussed some recent works in the healthcare domain such as disease surveillance, drug detection, disease prediction, etc., focusing on issues and challenges in machine learning–Natural Language Processing and use of medical ontologies as Social Media Mining Tools and Techniques that will help researchers and scientists working in this area. More research in the area is needed to be done considering rapid change of voluminous Social Media data which require efficient computation models as well as algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.