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
- Sharing of symptoms, information, and experiences as reported by frontline workers and people who were infected with the virus .
- Providing suggestions, opinions, and recommendations to reduce the spread of the virus .
- Communicating updates on vaccine development, clinical trials, and other forms of treatment .
- Sharing guidelines mandated by various policy-making bodies, such as mask mandate, social distancing, etc., that were required to be followed by members in specific geographic regions of the world under the authority of the associated policy-making bodies .
- Dissemination of misinformation such as the use of certain drugs or forms of treatment that have not been tested or have not undergone clinical trials .
- Creating and spreading conspiracy theories such as considering 5G technologies responsible for the spread of COVID-19, which eventually led to multiple 5G towers being burnt down in the United Kingdom .
- Studying public opposition to available vaccines in different parts of the world .
- The results from sentiment analysis showed that a majority of the tweets (50.5%) had a ‘neutral’ emotion, which was followed by the emotional states of ‘bad’, ‘good’, ‘terrible’, and ‘great’ that were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively.
- The results from tweet source tracking showed that 35.2% of the Tweets were posted from an Android source, which was followed by the Twitter Web App, iPhone, iPad, TweetDeck, and other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively.
- The results from tweet language interpretation showed that 65.9% of the tweets were posted in English, which was followed by Spanish or Castillian (10.5%), French (5.1%), Italian (3.3%), Japanese (2.5%), and other languages that accounted for <2% of the tweets.
- The results from tweet type classification showed that the majority of the tweets (60.8%) were retweets which was followed by original tweets (19.8%) and replies (19.4%).
- The results from embedded URL analysis showed that the most common domain embedded in the tweets was twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and a few other domains.
2. Literature Review
- Most of these works used approaches to detect tweets that contained one or more keywords, hashtags, or phrases such as “COVID-19”, “coronavirus”, “SARS-CoV-2”, “covid”, “corona,” etc., but none of these works focused on including one or more keywords directly related to the SARS-CoV-2 Omicron variant to include the associated tweets. As the SARS-CoV-2 Omicron variant is now responsible for most of the COVID-19 cases globally, the need in this context is to filter tweets that contain one or more keywords, hashtags, or phrases related to this variant.
- The works on sentiment analysis [88,89] focused on the proposal of new approaches to detect the sentiment associated with tweets; however, the categories for classification of the associated sentiment were only ‘positive’, ‘negative’, and ‘neutral’. In a realistic scenario, there can be different kinds of ‘positive’ emotions, such as ‘good’ and ‘great’. Similarly, there can be different kinds of ‘negative’ emotions, such as ‘bad’ and ‘terrible’. The existing works cannot differentiate between these kinds of positive or negative emotions. Therefore, the need in this context is to expand the levels of sentiment classification to include the different kinds of positive and negative emotions.
- While there have been multiple innovations in this field of Twitter data analysis, such as detecting trending topics , anomaly events , public perceptions towards C.D.C. , and views towards not wearing masks , just to name a few, there has been minimal work related to quantifying and ranking the associated insights.
- The number of tweets that were included in previous studies (such as 4081 tweets in  and 4492 tweets in ) comprises a very small percentage of the total number of tweets that have been posted related to COVID-19 since the beginning of the outbreak. Therefore, the need in this context is to include more tweets in the studies.
3. Materials and Methods
3.1. Compliance with Twitter Policies
3.2. Overview of Social Bearing
- While displaying the results, the tool shows a list of users (with their Twitter usernames), who posted the tweets, in the “All Contributors” section. It allows removing one or more users from this list (if the usernames indicate that the Twitter profiles are bots) so that the new results are obtained based on the tweets posted by the rest of the users.
3.4. Data Availability
3.4.1. Compliance with Guidelines for Twitter Content Redistribution
3.4.2. Compliance with FAIR
3.4.3. Data Description
3.4.4. Instructions for Using the Dataset
- Download and install the desktop version of the Hydrator app from https://github.com/DocNow/hydrator/releases (accessed on 14 May 2022).
- Click on the “Link Twitter Account” button on the Hydrator app to connect the app to an active Twitter account.
- Click on the “Add” button to upload one of the dataset files (such as Tweet IDs_November.txt). This process adds a dataset file to the Hydrator app.
- If the file upload is successful, the Hydrator app will show the total number of Tweet IDs present in the file. For instance, for the file—“TweetIDs_November.txt “, the app would show the number of Tweet IDs as 16,471.
- Provide details for the respective fields: Title, Creator, Publisher, and URL in the app, and click on “Add Dataset” to add this dataset to the app.
- The app will automatically redirect to the “Datasets” tab. Click on the “Start” button to start hydrating the Tweet IDs. During the hydration process, the progress indicator will increase, indicating the number of Tweet IDs that have been successfully hydrated and the number of Tweet IDs that are pending hydration.
- After the hydration process ends, a .jsonl file will be generated by the app that the user can choose to save on the local storage.
- The app would also display a “CSV” button in place of the “Start” button. Clicking on this “CSV” button would generate a .csv file with detailed information about the tweets, which would include the text of the tweet, User ID, username, retweet count, language, tweet URL, source, and other public information related to the tweet.
- Repeat steps 3–8 for hydrating all the files of this dataset.
4. Results and Discussions
- The previous works in this field proposed approaches to filter tweets based on one or more keywords, hashtags, or phrases such as “COVID-19”, “coronavirus”, “SARS-CoV-2”, “covid”, “corona” but did not contain any keyword or phrase specifically related to the Omicron variant. Given this, those approaches for tweet searching or tweet filtering might not be applicable to collect the tweets posted about the Omicron variant unless the Twitter user specifically mentions something like “COVID-19 omicron variant” or “SARS-CoV-2 Omicron variant” in their tweets. As discussed in Section 3, there were multiple instances when the Twitter users did not use keywords, hashtags, or phrases such as “COVID-19”, “coronavirus”, “SARS-CoV-2”, “covid”, “corona” along with the keyword or hashtag “Omicron”. Thus, the need is to develop an approach to specifically mine tweets posted about the Omicron variant. This work addresses this need by proposing a methodology that searches tweets based on the presence of “Omicron” either as a keyword or as a hashtag. The effectiveness of this approach is justified by the word clouds presented in Figure 8 and Figure 9.
- The prior works [88,89] on sentiment analysis of tweets about COVID-19 focused on developing approaches for classifying the sentiment only into three classes—‘positive’, ‘negative’, and ‘neutral’. In a realistic scenario, there can be different kinds of ‘positive’ emotions, such as ‘good’ and ‘great’. Similarly, there can be different kinds of ‘negative’ emotions, such as ‘bad’ and ‘terrible’. The existing works cannot differentiate between these kinds of positive or negative emotions. To address the need in this context, associated with increasing the number of classes for classification of the sentiment of the tweet, this work proposes an approach that classifies tweets into five sentiment classes: ‘great’, ‘good’, ‘neutral’, ‘bad’, and ‘terrible’ (Figure 3).
- The emerging works in this field, for instance, related to detecting trending topics , anomaly events , public perceptions towards C.D.C. , and views towards not wearing masks , focused on the development of new frameworks and methodologies without focusing on quantifying the multimodal components of the characteristics of the tweets and ranking these characteristics to infer insights about social media activity on Twitter due to COVID-19. This work addresses this need. The results from sentiment analysis, type detection, source tracking, language interpretation, and embedded URL observation were categorized into distinct categories, and these categories were ranked in terms of the associated characteristics to infer meaningful and relevant insights about social media activity on Twitter related to tweets posted about the SARS-CoV-2 Omicron variant (Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7). For instance, for tweet type analysis, the findings of this study show that “Twitter for Android” accounted for the most number of tweets (35.2% of the total tweets), which was followed by “Twitter Web App” (29.2% of the total tweets), “Twitter for iPhone” (25.8% of the total tweets), and other sources.
- The previous works centered around performing data analysis on tweets related to COVID-19, included a small corpus of tweets, such as 4081 tweets in  and 4492 tweets in . In view of the number of active Twitter users and the number of tweets posted each day, there is a need to include more tweets in the data analysis process. This work addresses this need by considering a total of 12,028 relevant tweets that had a combined reach of 149,500,959, with 226,603,833 impressions, 1,053,869 retweets, and 3,427,976 favorites.
- The development of Twitter datasets has been of significant importance and interest to the scientific community in the areas of Big Data mining, Data Analysis, and Data Science. This is evident from the recent Twitter datasets on 2020 U.S. Presidential Elections , 2022 Russia–Ukraine war , climate change , natural hazards , European Migration Crisis , movies , toxic behavior amongst adolescents , music , civil unrest , drug safety , and Inflammatory Bowel Disease . Twitter datasets help to serve as a data resource for a wide range of applications and use cases. For instance, the Twitter dataset on music  has helped in the development of a context-aware music recommendation system , next-track music recommendations as per user personalization , session-based music recommendation algorithms , music recommendation systems based on the use of affective hashtags , music chart predictions , user-curated playlists , sentiment analysis of music , listener engagement with popular songs , culture aware music recommendation systems , mining of user personalities , and several other applications. The works related to the development of Twitter datasets on COVID-19 [83,84,85,86,87] in the last few months did not focus on the development of a Twitter dataset comprising tweets about the Omicron variant of COVID-19 since the first detected case of this variant. To address this research gap, we developed a Twitter dataset (Section 3.4) that comprises 522,886 Tweet IDs of the same number of tweets about the Omicron variant of COVID-19 since the first detected case of this variant on 24 November 2021.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Tweet Source Label||Condition for Assignment|
|Twitter Web App||The tweet was posted by visiting the official website of Twitter |
|Twitter for Android||The tweet was posted using the Twitter app for Android operating systems, which is available for free download on the Google Playstore |
|Twitter for iPhone||The tweet was posted using the Twitter app for iPhone, which is available for free download on the App Store |
|TweetDeck||The tweet was posted by using TweetDeck, a social media dashboard application for the management of Twitter accounts |
|Filename||No. of Tweet IDs||Date Range of the Tweet IDs|
|TweetIDs_November.txt||16471||24 November 2021 to 30 November 2021|
|TweetIDs_December.txt||99288||1 December 2021 to 31 December 2021|
|TweetIDs_January.txt||92860||1 January 2022 to 31 January 2022|
|TweetIDs_February.txt||89080||1 February 2022 to 28 February 2022|
|TweetIDs_March.txt||97844||1 March 2022 to 31 March 2022|
|TweetIDs_April.txt||91587||1 April 2022 to 20 April 2022|
|TweetIDs_May.txt||35756||1 May 2022 to 12 May 2022|
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Thakur, N.; Han, C.Y. 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. COVID 2022, 2, 1026-1049. https://doi.org/10.3390/covid2080076
Thakur N, Han CY. 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. COVID. 2022; 2(8):1026-1049. https://doi.org/10.3390/covid2080076Chicago/Turabian Style
Thakur, Nirmalya, and Chia Y. Han. 2022. "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" COVID 2, no. 8: 1026-1049. https://doi.org/10.3390/covid2080076