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

Abstract

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, knowledge, 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 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the 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 or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. 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”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. 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, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021.

Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

“…An exploratory study and sentiment analysis on a big dataset about the COVID-19 pandemic focusing on the Omicron variant was proposed by Thakur N. et al [32]. The role of Twitter during the COVID-19 pandemic in spreading information and misinformation was presented through sentiment analysis [33].…”

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

“…This is a significant increase as compared to recent historical data as far as a high number of Google Searches (indicated by the search interest value being 100) related to Disease X are The internet serves as a platform for public health organizations to efficiently and affordably distribute healthcare information. However, it is crucial that reliable news is disseminated to the general public [115][116][117]. In the last few weeks, many public health organizations have disseminated information pertaining to Disease X, with the aim of reaching a wide audience [118][119][120][121].…”

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

“…Some of the recent works in this field include Twitter datasets on hate speech and abusive language [ 48 ], the European migration crisis [ 49 ], natural hazards [ 50 ], misogynistic language [ 51 ], offensive language [ 52 ], civil unrest [ 53 ], exoskeletons [ 54 ], the efficacy of hydroxychloroquine as a treatment for COVID-19 [ 55 ], pregnancy outcomes [ 56 ], drug-related knowledge [ 57 ], the public opinion of people in Indonesia on different matters [ 58 ], a severe storm and F1 tornado that struck Central Pennsylvania [ 59 ], online learning during the COVID-19 Omicron wave [ 60 ], multi-ideology or white supremacy [ 61 ], Sundanese (the second-largest tribe in Indonesia) [ 62 ], vaccines [ 63 ], BlackLivesMatter movement [ 64 ], the Omicron variant of COVID-19 [ 65 ], hazardous events at the Baths of Diocletian site in Rome [ 66 ], memes from Black Twitter [ 67 ], and the Arabic language [ 68 ]. In addition to this, the outbreak of COVID-19 was associated with the development of multiple Twitter datasets, such as Twitter datasets on conversations about COVID-19 in Spanish [ 69 ], Bengali [ 70 ], and English [ 71 ].…”

“…Performing a comprehensive analysis of Tweets related to virus outbreaks, pandemics, and epidemics has been of significant interest to researchers in this field in the recent past. In [ 65 ], the authors presented a study on Tweets posted about the Omicron variant of COVID-19. The specific characteristics of Tweets that were studied included sentiment, language usage, Tweet source, Tweet types (retweets, original Tweets, and replies), and embedded URLs.…”