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This paper presents the findings of an exploratory study on the continuouslygenerating 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 dominantvariant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicronvariant were studied, and the specific characteristics of tweets that wereanalyzed include - sentiment, language, source, type, and embedded URLs. Thefindings of this study are manifold. First, from sentiment analysis, it wasobserved 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 thetweets, respectively. Second, the findings of language interpretation showedthat 65.9% of the tweets were posted in English. It was followed by Spanish,French, Italian, and other languages. Third, the findings from source trackingshowed that Twitter for Android was associated with 35.2% of tweets. It wasfollowed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and othersources. Fourth, studying the type of tweets revealed that retweets accountedfor 60.8% of the tweets, it was followed by original tweets and replies thataccounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms ofembedded URL analysis, the most common domain embedded in the tweets was foundto be twitter.com, which was followed by biorxiv.org, nature.com, and otherdomains. Finally, to support similar research in this field, we have developeda Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2omicron variant since the first detected case of this variant on November 24,2021.

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant:  Insights from Sentiment Analysis, Language Interpretation, Source Tracking,  Type Classification, and Embedded URL Detection