  Background: Social media platforms have become a viable source of medical
information, with patients and healthcare professionals using them to share
health-related information and track diseases. Similarly, YouTube, the largest
video-sharing platform in the world contains vlogs where individuals talk about
their illnesses. The aim of our study was to investigate the use of Natural
Language Processing (NLP) to identify the spoken content of YouTube vlogs
related to the diagnosis of Coronavirus disease of 2019 (COVID-19) for public
health screening. Methods: COVID-19 videos on YouTube were searched using
relevant keywords. A total of 1000 videos being spoken in English were
downloaded out of which 791 were classified as vlogs, 192 were non-vlogs, and
17 were deleted by the channel. The videos were converted into a textual format
using Microsoft Streams. The textual data was preprocessed using basic and
advanced preprocessing methods. A lexicon of 200 words was created which
contained words related to COVID-19. The data was analyzed using topic
modeling, word clouds, and lexicon matching. Results: The word cloud results
revealed discussions about COVID-19 symptoms like "fever", along with generic
terms such as "mask" and "isolation". Lexical analysis demonstrated that in
96.46% of videos, patients discussed generic terms, and in 95.45% of videos,
people talked about COVID-19 symptoms. LDA Topic Modeling results also
generated topics that successfully captured key themes and content related to
our investigation of COVID-19 diagnoses in YouTube vlogs. Conclusion: By
leveraging NLP techniques on YouTube vlogs public health practitioners can
enhance their ability to mitigate the effects of pandemics and effectively
respond to public health challenges.
