  This paper presents the findings of an exploratory study on the continuously
generating Big Data on Twitter related to the sharing of information, news,
views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic,
with a specific focus on the Omicron variant, which is the globally dominant
variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron
variant were studied, and the specific characteristics of tweets that were
analyzed include - sentiment, language, source, type, and embedded URLs. The
findings of this study are manifold. First, from sentiment analysis, it was
observed that 50.5% of tweets had a neutral emotion. The other emotions - bad,
good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the
tweets, respectively. Second, the findings of language interpretation showed
that 65.9% of the tweets were posted in English. It was followed by Spanish,
French, Italian, and other languages. Third, the findings from source tracking
showed that Twitter for Android was associated with 35.2% of tweets. It was
followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other
sources. Fourth, studying the type of tweets revealed that retweets accounted
for 60.8% of the tweets, it was followed by original tweets and replies that
accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of
embedded URL analysis, the most common domain embedded in the tweets was found
to be twitter.com, which was followed by biorxiv.org, nature.com, and other
domains. Finally, to support similar research in this field, we have developed
a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2
omicron variant since the first detected case of this variant on November 24,
2021.
