Analyzing Public Concerns Over COVID-19 Variants Using Social Media

Authors

DOI:

https://doi.org/10.26713/cma.v13i1.1977

Keywords:

Social media analysis, Tweet analysis, Covid tweet, Covid hashtag, Number of posts, Virus expression, Viruses and cancer, Viral diseases

Abstract

SARS-CoV-2, or more popularly known COVID-19 has claimed more than 5.5 million lives since it has been declared as a global pandemic. Similar to other viruses, COVID-19 is also undergoing several mutations and has many variants like Alpha, Beta, Gamma, Delta, Omicron and others. With so many variants, social media users are confused and posting their frustrations and angers with Tweets or Posts in public social media platforms. These publicly accessible social media posts provide a wealth of information for a social scientist or political leader or a strategic decision maker. This study demonstrates a feasible approach to extract meaningful critical information from social media posts. By programmatically accessing Twitter database from 11th January 2022 till 20th January 2022, we retrieved almost 9 K Tweet messages on 6 different keywords like “COVID Variants”, “Omicron”, “Alpha Variant”, “Beta Variant”, “Gamma Variant” and “Delta Variant”. Results were compared against metrics like users, posts, engagement, and influence. Omicron was found to be the most popular topic compared to other variants with an influence score of 70.2 million and 2.1 K posts during the monitored period. The most popular sources for influences on COVID-19 Variant related posts were found to be @reuters with 24.2M, @forbes with 17.4M, @timesofindia with 14.2M and @inquirerdotnet with 3.4 followers. This study also found out that the most popular Tweet languages were English followed by French and Dutch. Lastly, this study ranked user mentions, word frequency (with word cloud) and hashtags for COVID-19 Variant related twitter posts during the monitored timeframe.

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References

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Published

23-05-2022
CITATION

How to Cite

Alsulami, M. (2022). Analyzing Public Concerns Over COVID-19 Variants Using Social Media. Communications in Mathematics and Applications, 13(1), 379–386. https://doi.org/10.26713/cma.v13i1.1977

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Section

Review Article