The Evaluation of Distributed Topic Models for Recognition of Health-Related Topics in Social Media Through Machine Learning Paradigms

Abstract

Social media is the most effective technique to obtain enormous amounts of data on tweets from the health field internationally. It is also a well-known source of data for anticipating healthcare-related solutions and looking for health-related phrases. In terms of income and employment, the health care industry has grown to become one of the biggest in the world. People may share their opinions and thoughts on a range of healthcare-related issues on Twitter, which is used by billions of users every day.  The research gap in topic modeling related to healthcare topics in social media refers to areas or aspects that have not been extensively explored or adequately addressed by existing studies. Here are a few potential research gaps in this domain. Many studies focus on general healthcare discussions in social media, but there may be specific healthcare topics or subdomains that have not received sufficient attention. Research could focus on exploring topic modeling techniques for niche healthcare areas like mental health, rare diseases, specific treatments, or emerging healthcare technologies. Most topic modeling studies in social media healthcare discussions do not account for the user context and demographics. Research could investigate the influence of user characteristics, such as age, gender, location, or occupation, on the topics discussed, providing a deeper understanding of how different demographics engage with healthcare topics. Social media platforms are highly dynamic, and the popularity and sentiment of healthcare topics can change rapidly. There is a need for research that focuses on analyzing the temporal dynamics of healthcare topics in social media and tracking the evolution of topics over time. Social media platforms not only consist of text-based content but also include visual and audiovisual data. Research could explore topic modeling techniques that can effectively integrate and analyze multimodal data, such as images, videos, or audio, in healthcare-related discussions. While various evaluation metrics exist for topic modeling, they may not capture the unique characteristics and challenges of healthcare-related discussions in social media. Developing domain-specific evaluation metrics or adapting existing metrics to better assess the quality and relevance of topics in healthcare-related social media data is an important research direction. Social media data often raises ethical concerns related to privacy, consent, and data usage. Research gaps exist in exploring ethical guidelines, data anonymization techniques, and best practices for conducting topic modeling research on healthcare topics in social media while ensuring privacy and confidentiality. Addressing these research gaps can contribute to a more comprehensive understanding of healthcare topics in social media discussions and provide valuable insights for healthcare practitioners, policymakers, and researchers. It can help identify emerging healthcare trends, public sentiment, and inform evidence-based decision-making in the healthcare domain. The main objective of this research era is By applying topic modeling methods such as CvLDA and DiCTM to healthcare topics in social media, researchers and practitioners can gain insights into the prevalent themes, concerns, and discussions in the online healthcare domain. It enables the identification of emerging topics, the monitoring of public perceptions and sentiments, the discovery of valuable information for public health interventions, and the understanding of patient experiences and needs in the digital space.