Comparative Analysis of Telemedicine in Media Coverage Pre- and Post-COVID-19 using Unsupervised Latent Dirichlet Topic Modeling

Abstract: Telemedicine, driven by technology, has become a game-changer in healthcare, with the COVID-19 pandemic amplifying its significance by necessitating remote healthcare solutions. This study explores the evolution of telemedicine through news big data analysis. Our research encompassed a vast dataset from 51 media outlets (total 28,372 articles), including national and regional dailies, economic newspapers, broadcasters, and professional journals. Using LDA analysis, we delved into pre- and post-pandemic telemedicine trends comprehensively. A crucial revelation was the prominence of "medical law" in telemedicine discussions, underscoring the need for legal reforms. Keywords like "artificial intelligence" and "big data" underscored technology's pivotal role. Post-pandemic, keywords like "COVID-19," "online healthcare," and "telemedicine" surged, reflecting the pandemic's impact on remote healthcare reliance. These keywords' increased frequency highlights the pandemic's transformative influence. This study stresses addressing healthcare's legal constraints and maximizing technology's potential. To seamlessly integrate telemedicine, policy support and institutional backing are imperative. In summary, telemedicine's rise, propelled by COVID-19, signifies a healthcare paradigm shift. This study sheds light on its trajectory, emphasizing legal reforms, tech innovation, and pandemic-induced changes. The post-pandemic era must prioritize informed policy decisions for telemedicine's effective and accessible implementation.

Haewon Byeon. “Comparative Analysis of Telemedicine in Media Coverage Pre- and Post-COVID-19 using Unsupervised Latent Dirichlet Topic Modeling”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.4 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150404

@article{Byeon2024,
title = {Comparative Analysis of Telemedicine in Media Coverage Pre- and Post-COVID-19 using Unsupervised Latent Dirichlet Topic Modeling},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150404},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150404},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Haewon Byeon}
}