# 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 arXiv: https://arxiv.org/abs/2208.10252 ยท HTML: https://mlsanity.com/paper/2208.10252/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 137 references that are in the map. - First posted on arXiv 20 Jul 2022. ## Builds on (most cited first, 15 of 15) - [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018) - [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020) - [ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter](https://mlsanity.com/paper/2008.06465/) (2020) - [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](https://mlsanity.com/paper/2010.08768/) (2020) - [Large Arabic Twitter Dataset on COVID-19](https://mlsanity.com/paper/2004.04315/) (2020) - [Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter](https://mlsanity.com/paper/1707.03778/) (2017) - [Twitter Dataset for 2022 Russo-Ukrainian Crisis](https://mlsanity.com/paper/2203.02955/) (2022) - [Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder](https://mlsanity.com/paper/2009.03947/) (2020) - [Social Media Mining Toolkit (SMMT)](https://mlsanity.com/paper/2003.13894/) (2020) - [Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information](https://mlsanity.com/paper/1405.3750/) (2014) - [Natural Hazards Twitter Dataset](https://mlsanity.com/paper/2004.14456/) (2020) - [Detecting and monitoring foodborne illness outbreaks: Twitter communications and the 2015 U.S. Salmonella outbreak linked to imported cucumbers](https://mlsanity.com/paper/1708.07534/) (2017) - [A large-scale Twitter dataset for drug safety applications mined from publicly existing resources](https://mlsanity.com/paper/2003.13900/) (2020) - [Dutch General Public Reaction on Governmental COVID-19 Measures and Announcements in Twitter Data](https://mlsanity.com/paper/2006.07283/) (2020) - [BOTTER: A framework to analyze social bots in Twitter](https://mlsanity.com/paper/2106.15543/) (2021) ## Built on by (most cited first, 7 of 7) - [COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations](https://mlsanity.com/paper/2412.17180/) (2024) - [Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset](https://mlsanity.com/paper/2405.04897/) (2024) - [A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles](https://mlsanity.com/paper/2406.07693/) (2024) - [Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2410.03293/) (2024) - [Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index](https://mlsanity.com/paper/2412.16925/) (2024) - [Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework](https://mlsanity.com/paper/2503.11845/) (2025) - [Content and Engagement Trends in COVID-19 YouTube Videos: Evidence from the Late Pandemic](https://mlsanity.com/paper/2509.01954/) (2025) ## Abstract (arXiv) 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.