GeoCovaxTweets: A global analysis of COVID-19 vaccines and vaccination discourse on social media

Abstract

Social media platforms play a crucial role in providing valuable information during crises, such as pandemics. The COVID-19 pandemic has created a global public health crisis, and vaccines are the key preventive measure for achieving herd immunity. However, some individuals use social media to oppose vaccines, undermining government efforts to eliminate the virus. This study introduces the “GeoCovaxTweets” dataset, consisting of 1.8 million geotagged tweets related to COVID-19 vaccines from January 2020 to November 2022, originating from 233 countries and territories. Each tweet includes state and country information, enabling researchers to analyze global spatial and temporal patterns. An extensive set of analyses are performed on the dataset to identify prominent topic clusters and explore public opinions across different vaccines and vaccination contexts. The study outlines the dataset curation methodology and provides instructions for local reproduction. We anticipate that the dataset will be valuable for crisis computing researchers, facilitating the exploration of Twitter conversations surrounding COVID-19 vaccines and vaccination, including trends, opinion shifts, misinformation, and anti-vaccination campaigns.

Keywords

COVID-19 discourse COVID-19 pandemic sentiment analysis social media topic clustering twitter dataset

1 Introduction

The COVID-19 outbreak in December 2019 resulted in more than six million deaths cases and 600 million confirmed cases globally [36]. The respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a public health emergency of international concern on January 30, 2020, and a pandemic on March 13, 2020, by World Health Organization. Vaccines such as Pfizer, Oxford-AstraZeneca, Johnson and Johnson, and Moderna were started in late 2020 to control the spread [35]. To acquire herd immunity to end the pandemic, it has been estimated that around 60-70% of the world population must get vaccinated [1]. However, one of the biggest hindrances to vaccinations is hesitancy in acceptance due to numerous reasons, importantly perceived fears. Myths and fears around vaccines are not new. The COVID-19 pandemic has brought vaccine hesitancy back to the limelight. Social media, which provides real-time access to people’s opinions and beliefs across demographics, has frequently been signified as a hotbed of activity for anti-vaccination activists [18]. These activists may turn to social media platforms to oppose vaccinations with claims lacking scientific support. Metaphors used to argue against vaccination revolve around negative phrases such as “no forced vaccines” and “say no to vaccines”. A study [20] reported that during February and March 2021, 40–47% of American adults were hesitant towards COVID-19 vaccinations. Previous studies have linked social media and the anti-vaccination movement towards vaccine hesitancy [4, 15]. The COVID-19 vaccines and vaccination-specific discussions on social media platforms have influenced people’s decisions to accept or reject vaccination. Therefore, understanding the nature of vaccine hesitancy through publicly available social media discussions for different geographical regions can open interesting research avenues in the crisis computing domain.

Numerous COVID-19-specific datasets have been released to study the nature of COVID-19 and its vaccines and vaccination contexts. [3, 6, 12, 21, 25] are some of the pubicly available large-scale COVID-19 datasets that have hundreds of millions of tweets. However, these datasets vaguely focus on general COVID-19 discussions as their keyword sets are not entirely focused on vaccines and vaccination specific discourse. Closely focused datasets have also been introduced [7, 28, 30]; however, such datasets are either focused on a particular geographical region or have limited temporal coverage. To bridge these gaps, we present a large-scale geotagged tweets dataset, GeoCovaxTweets, containing vaccines and vaccination-related Twitter conversations with geolocation data. This dataset covers various perspectives on COVID-19 vaccines and vaccination, as our keywords and hashtags set capture a comprehensive conversational dynamics of support, criticism, and hesitance towards the COVID-19 vaccines and vaccination. Besides introducing this novel dataset, we also explore the dataset through topical and sentimental dimensions to generate an outlook on the COVID-19 vaccines and vaccinations over a period of nearly 34 months, i.e., January 2020–November 2022. In general, our contributions to the existing crisis informatics literature are summarized below:

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We present GeoCovaxTweets, a large-scale global geotagged tweets dataset covering a 34-month period from the first COVID-19 vaccine announcement to the availability of booster doses. This dataset includes a comprehensive conversational dynamics of support, criticism, and hesitance towards COVID-19 vaccines and vaccination. Additionally, we share normalized state names for each collected tweet and country information, ensuring precise geolocation information. The dataset can be accessed from Harvard Dataverse (https://doi.org/ 10.7910/DVN/YTH2MM). We release only a few tweet objects, including tweet identifiers, to comply with Twitter’s policy. Researchers can retrieve the original tweets and related metadata using tools such as twarc and Hydrator.
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We conduct topic modelling on the GeoCovaxTweets dataset to identify clusters of topics associated with vaccines and vaccination discussions. We assign names to these topic clusters based on the content of the tweets within each cluster. Our analysis reveals the existence of 142 topics at the global level, with each topic being discussed by a minimum of 1,000 geotagged tweets. To our knowledge, this comprehensive analysis represents the first instance of such an endeavour. We publicly release the trained topic model to enable further research.
–
We analyze the tweets globally and across the top 6 countries that are more frequent in the discourse to identify prominent global and country-specific discussion clusters.

2 Related work

Several researchers collect and share large-scale COVID-19 tweets datasets. [3, 6, 21, 25, 32] provide large-scale tweet collections with different data distributions, collection periods, and sets of keywords. [3, 6] present multilingual datasets with 2.5 billion and 1.12 billion tweets, respectively. Similarly, [12] has 2 billion multilingual tweets, with the last release on March 2021. [21] maintains an English-only collection with more than 2.2 billion tweets; its re-hydrated version is also available [25]. The keywords sets differ amongst the datasets—[6] use 80 keywords, [3] use 10 keywords, [21] uses 90+ keywords, [12] use 800+ multilingual keywords. The common issue with these billion-scale datasets is that they are curated to capture the conversational dynamics of COVID-19 across every thematic area. In doing so, due to limits (per day upper limit and sample size) set by Twitter on its filtered stream endpoint, these datasets contain proportionately fewer theme-specific tweets compared to theme-dedicated collections.

Vaccines and vaccination-dedicated collections have also been released. [30] presented a dataset of 137 million tweets with antivaccine discussions. The dataset includes tweets fetched through two complementary collections: first, using 60 keywords through streaming API, and second, collecting historical tweets from 70k accounts that allegedly propagated anti-vaccine tweets. [7] introduced a dataset of 4.7 million tweets that includes one week of vaccine-related tweets and designed a dashboard to track and quantify credible information and misinformation records along with their sources and keywords. Similarly, [10] presented spatio-temporal trends from March 01, 2022, to February 08, 2022, toward COVID-19 vaccines in the United States.

Several studies have utilized topic modelling and sentiment analysis to investigate public perceptions and identify prevalent concerns in COVID-19 vaccines and vaccination-specific discussions on Twitter. [31] performed topic modelling using vanilla LDA and identified 12 topics/themes from pro-vaccines and anti-vaccine group tweets. Similarly, [27] analyzed COVID-19 vaccine-related tweets and identified 16 topics grouped into five themes, with opinions about vaccination being the most tweeted topic. [38] extracted prevalent words in location-based tweets discussing vaccine brands, analyzed sentiment using VADER, and employed LDA to identify topics in positive and negative tweets. [11] used sentiment-based topic modelling, utilizing compound scores for sentiment polarity and coherence scores to identify the optimal topic number for various sentiment polarity categories. [17] analyzed tweet sentiment using the Brandwatch tool, measured the frequency of negative and non-negative tweets, and extracted 26 topics related to COVID-19 vaccine discussions. [29] employed nonnegative matrix factorization for topic identification and VADER for sentiment analysis, revealing concerns about vaccine administration and access, with fear being the predominant emotion followed by joy. [27] developed a three-tier model, encompassing sentiment-aware topic extraction, emotion-aware analysis of user reactions to topics, and a comprehensive three-perspective topic relevance analysis, within the context of a COVID-19 tweet dataset. [27] used VADER and the sentiment reasoner tool to calculate compound scores and classify tweet sentiments as positive, neutral, or negative. They conducted temporal and geographic analyses to identify trends and variations in sentiment across different locations. [2] discussed a simple attention-based neural network model following the transformer architecture, applying it to the task of analyzing sentiment in tweets. [34] employed artificial intelligence and geospatial methods to analyze tweets, determine sentiment polarity using the BERT model, and visualize sentiments on a world map. Hotspot analysis and kernel density estimation were applied to identify regions with positive, negative, or neutral sentiments.

However, these studies have limitations in terms of duration and focus mainly on the early stages of the pandemic. Thus, there is a need for research that explores the evolving nature of public perception of COVID-19 vaccines over an extended period.

To address the aforementioned gaps, we present a large-scale dataset of geotagged tweets GeoCovaxTweets related to COVID-19 vaccines and vaccination, with a cross-national, covers a period of approximately 34 months, from January 2020 to November 2022. We used Twitter’s Full-archive search endpoint to collect historical tweets, which returns 100% of Twitter data for a given query condition, unlike filtered and standard search endpoints used in previous studies that only return 1% of Twitter data at a particular time. We also performed topic clustering and sentiment analysis on GeoCovaxTweets to identify (pro-anti-neutral) stances and prominent clusters that emerge from discussions related to COVID-19 vaccines and vaccination. Overall, we believe that the released dataset will assist researchers in the crisis computing domain in analyzing Twitter conversations to explore global spatiotemporal shifts and trends related to the COVID-19 vaccine narrative.

3 Methods

3.1 Data collection

We used twarc python library to utilize Twitter’s Full-archive search endpoint. The endpoint returns all historical tweets, unlike the standard search endpoint, which returns <1% of tweets at a particular time. To collect tweets relevant to COVID-19 vaccines and vaccination, we used meta-seed keywords having pro and anti-vaccine stances through a snowball sampling technique [37]. We started with the following seed keywords: CovidVaccineScam, NoForcedVaccines, covidvaccineVictims, BanCovidVaccine, getvaccinated, covishield affect, Covaxin affect, vaccineinjuries, NoForcedVaccination, vaccine, and vaccination. Moving on, we analyzed N-grams in the collected corpus every 10 minutes to track emerging relevant keywords, similar to the strategy applied in [21]. Furthermore, we refined the keyword list by identifying potential keywords that frequently co-occur with the seed keywords [7]. We provide the complete set of keywords and hashtags used for curating GeoCovaxTweets in Table 1. Furthermore, we applied has:geo and lang:en conditional operators to collect only English-language tweets that are geotagged. The complete data curation process is illustrated in Fig. 1.

Table 1
Keywords and hashtags used in curating GeoCovaxTweets

vaccine, vaccines, vaccination, vaccinations, #vaccine, #vaccines, #vaccination, #vaccinations, #coronavaccine, #CovidVaccineScam, #covidvaccinedeaths, #covidvaccineVictims, #CovidVaccineKills, #crimesagainsthumanity, #BanCovidVaccine, #vaxxdamage, #CovidVaccinesKill, Antivaccine, ****vaccines, forcedvaccines, NoVaccineForMe, NoVaccine, NoForcedVaccines, vaccinefailure, vaccinefraud, vaccinedamage, vaccineharm, unvaccinated, saynotovaccines, notomandatoryvaccines, covidvaccineispoison, learntherisk, vaccineinjuries, vaccineinjury, NoForcedVaccines, vaccinefailure, depopulation, stopmandatoryvaccination, covaxin serious problems, covishield serious problems, COVAXIN new born calf serum, Luc Montagnier’s claim, Luc Montagnier’s vaccine statement, abolishbigpharma, notocoronavirusvaccines, educateb4uvax, getvaccinated, iwillgetvaccinated, #Covaxin affect, #COVISHIELD affect, #pfizervaccine affect, #PfizerKills, #modernavaccineaffect, #Sputnikvaccine affect, #SputnikV unsafe, #Astrazenica affect, #oxfordvaccine affect, #VaccineSaveLives, #VACCINESWORK

Fig. 1

The data curation process.

3.2 State normalization

For each collected tweet, we also provide state information besides country information. Although the geo.full_name tweet object contains state information, some tweets had [city, state] or [state, country] information—for instance, [Mumbai, Maharastra], and [Maharastra, India]—and therefore normalization was required for consistency. We set up a planet-level geocoding endpoint powered by OpenStreetMap ¹ data. This endpoint allows us to obtain geolocation information on a global scale. The endpoint was backed with a search index of 61 gigabytes² (when compressed) on a virtual machine with 24 VCPUs and 216 gigabytes of memory. The endpoint returned state information for each [city, state] or [state, country] request.

3.3 Topic cluster and sentiment analysis

We utilized BERTopic [9], leveraging contextual embeddings derived from sentence transformers [33] to cluster topics through a class-based term frequency-inverse document frequency approach [9]. We experimented with multiple minimum cluster sizes, between 10 and 1000. At a minimum cluster size of 1000, we observed that the similarities (Cosine) between the generated topics (their embeddings) were minimal. Increasing the minimum cluster size further would reduce the number of topics; therefore, based on the quality of the topics that were being generated and the similarity matrices (such as shown in Fig. 4) across different settings, we finalized the minimum cluster size value to 1000. Algorithm 1 details our approach for topic generation using BERTopic.

For sentiment analysis, we use BERTsent [24], a RoBERTa-based sentiment classifier fine-tuned for English language tweets and trained on the SemEval 2017 corpus. After running BERTsent over GeoCovaxTweets, the sentiment of each tweet was classified as either negative, positive, or neutral.

Algorithm 1 BERTopic Topic Clustering

1: Input: Given a corpus of documents represented as C = {d ₁, d ₂, …, d _n}, where n is the total number of documents.

2: Text Preprocessing:

3: for each document d _i in C

4: Tokenize d _i to obtain Tokenized _{d
_i}.

5: Convert Tokenized _{d
_i} to lowercase to obtain Lowercased _{d
_i}.

6: Remove URLs, numbers, and special characters from Lowercased _{d
_i} to obtain Cleaned _{d
_i}.

7: end for

8: Document Embeddings using BERT:

9: for each cleaned document Cleaned _{d
_i}

10: Apply BERT to Cleaned _{d
_i} to obtain the document embedding Embedding _{d
_i}.

11: end for

12: Clustering:

13: Use the HDBSCAN clustering algorithm on the document embeddings {Embedding _{d
₁}, Embedding _{d
₂}, …, Embedding _{d
_n}} to group similar documents into clusters.

14: Assign each document to a cluster, resulting in Cluster _ Assignment _i.

15: Class-based TF-IDF to find representative words for each cluster:

16: for each cluster

17: Group all documents belonging to the cluster as a single document.

18: for each word w in the cluster

19: Compute TF-IDF score tf _ idf (w, d _i) = tf (w, d _i) × idf (w), where:

20: tf (w, d _i) is the number of times the word w appears in document d _i (Term Frequency).

21: idf (w) is the Inverse Document Frequency of the word w across all documents.

22: end for

23: Select the top words with the highest TF-IDF scores as representative words for the cluster.

24: end for

25: Topic Representation:

26: Represent each cluster with its assigned documents to form topics.

27: Store the topics in a dictionary called Topics, where each key-value pair represents a topic: {Topic1 : [d ₁, d ₅, d ₇] , Topic2 : [d ₂, d ₃, d ₆] , …}.

28: Output: Topics, a dictionary containing the topic ID as the key and a list of documents representing the topic as the value.

4 Discussions

4.1 Data description

The GeoCovaxTweets dataset has a total of 1,818,253 geotagged tweets created by 464.9k users between January 01, 2020, and November 25, 2022. Figure 2 presents the daily distribution of the tweets and the cumulative number of people vaccinated per hundred globally. The source of the vaccination data is Our World in Data ³. Out of 1.8 million tweets, 899,592 (49.5%) are from the United States, 263,765 (14.5%) from the United Kingdom, 160,913 (8.8%) from India, 136,389 (7.5%) from Canada, and 65,951 (3.6%) from Australia listed in Table 2. The top 6 countries contributing the highest number of English-language tweets in GeoCovaxTweets are the United States, the United Kingdom, India, Canada, Australia, and South Africa. Consequently, we conducted a detailed study focusing on these countries. Figure 3 presents the daily distribution of tweets in these 6 countries, along with the corresponding cumulative vaccination per hundred data. The tweet distribution was analyzed for the top 6 countries in the study. Figure 3 clearly demonstrates that the number of daily tweets related to vaccines and vaccination was lower before the commencement of the vaccination campaigns. Following the rollout of vaccines in different countries, the number of tweets in each country began to increase a few weeks prior to the official vaccination launch date. Once the vaccination process commenced, there was a significant surge in tweets across all countries.

Fig. 2

The daily global distribution of the tweets and the cumulative number of people vaccinated per hundred.

Table 2

The top 15 countries, states, and tweet sources in GeoCovaxTweets. Note: ^* OpenStreetMap data returned type:state for these countries

Country	Frequency	State	Frequency	Sources	Frequency
United States	899,592	England^*	218,477	Twitter for iPhone	958,579
United Kingdom	263,765	California	125,590	Twitter for Android	776,786
India	160,913	New York	75,308	Twitter for iPad	31,223
Canada	136,389	Ontario	69034	Instagram	16,154
Australia	65,951	Texas	68,067	dlvr.it	11,064
South Africa	40,243	Florida	51,433	Tweetbot for iOS	6,450
Ireland	31,529	Illinois	31,939	Twitter Web App	5,152
Philippines	14,992	Maharashtra	29,976	Twitter	2,020
Malaysia	12,856	Ohio	29,719	Twitter for Mac	1,806
New Zealand	11,895	Pennsylvania	28,791	Foursquare	1,109
Nigeria	11,711	Scotland^*	24,553	Tweetbot for Mac	959
Pakistan	8,943	Washington	23,647	Hootsuite Inc.	850
Germany	7,910	Georgia	23,558	TweetCaster for Android	664
Kenya	7,853	Massachusetts	22,751	Foursquare Swarm	602
Spain	6,416	Victoria	21,933	Vaccine Radar	563

Fig. 3

The daily distributions of tweets in the top 6 countries in the discourse along with their respective cumulative vaccination data. The un-uniformity in the vaccination data for some countries is due to irregular updates.

Regarding the statewide distribution, England, California, New York, Ontario, and Texas contribute the highest number of tweets. We also explored the different tweet sources in GeoCovaxTweets. Twitter’s native application for iPhone, Android, and iPad apps are the top 3 sources contributing more than 1.7 million tweets. The list also includes Instagram and dlvr.it in the top 5 generating above 10k tweets. We provide the top 15 countries, states, and tweet sources with their respective frequencies in Table 2.

We explored the top n-grams in the vaccines and vaccination-related discourse. We removed noisy data such as URLs, retweet information, special characters, paragraph breaks, and stop words before extracting bigrams and trigrams. Table 3 lists the top 15 bigrams and trigrams in GeoCovaxTweets. Similarly, we extracted the 15 top-tweeted hashtags, which are as follows: #covid19, #vaccine, #getvaccinated, #vaccination, #covid, #covidvaccine, #vaccines, #vaccineswork, #coronavirus, #covid_19, #vaccinated, #pfizer, #wearamask, #covid19vaccine, and #astrazeneca.

Table 3

The top 15 bigrams and trigrams in GeoCovaxTweets

Bigram	Frequency	Trigram	Frequency
(‘covid’, ‘19’)	121,032	(‘covid’, ‘19’, ‘vaccine’)	48,653
(‘covid’, ‘vaccine’)	72,345	(‘covid’, ‘19’, ‘vaccines’)	12,428
(‘get’, ‘vaccine’)	59,999	(‘covid’, ‘19’, ‘vaccination’)	10,905
(‘19’, ‘vaccine’)	50,897	(‘j’, ‘& ’, ‘j’)	7,671
(‘get’, ‘vaccinated’)	23,756	(‘get’, ‘covid’, ‘vaccine’)	5,243
(‘take’, ‘vaccine’)	21,475	(‘johnson’, ‘& ’, ‘johnson’)	3,880
(‘getting’, ‘vaccine’)	19875	(‘get’, ‘covid’, ‘19’)	3,782
(‘side’, ‘effects’)	18,847	(‘& ’, ‘j’, ‘vaccine’)	3,359
(‘covid19’, ‘vaccine’)	18,001	(‘vaccine’, ‘covid’, ‘19’)	3,119
(‘covid’, ‘vaccines’)	16,835	(‘first’, ‘dose’, ‘vaccine’)	3,012
(‘get’, ‘covid’)	14,819	(‘dose’, ‘covid’, ‘19’)	2,947
(‘pfizer’, ‘vaccine’)	14,262	(‘dose’, ‘covid’, ‘vaccine’)	2,852
(‘got’, ‘vaccine’),	13,911	(‘first’, ‘dose’, ‘covid’)	2,499
(‘first’, ‘dose’)	13,392	(‘vaccine’, ‘side’, ‘effects’)	2,481
(‘wear’, ‘mask’)	12,160	(‘getting’, ‘covid’, ‘vaccine’)	2,407

4.2 Topic clustering

We identified 142 topics, each with at least 1000 tweets, and listed them in Table 4. The listed topics include clusters that discuss various contextual aspects of COVID-19 vaccines and vaccinations, such as vaccine mandates (ethics, politics, personal autonomy), COVID-19 vaccination (effectiveness, side effects, public perception), vaccine distribution (challenges, disparities, global impact), vaccine misinformation (campaigns, conspiracy theories, social media), vaccine development (progress, challenges, breakthrough), COVID-19 variants (vaccination strategies, effectiveness, global response), vaccine passports (implementation, privacy concerns, public acceptance), vaccination for children (efficacy, safety, ethical considerations), COVID-19 and public health (lockdowns, testing, vaccine policies), and vaccine cost and affordability (global access, equity, and financing).

Table 4
Most discussed topic clusters in GeoCovaxtweets. Topics are sorted based on the volume of tweets

# Topic # Topic

0 Vaccines and Public Perception. 71 Compulsory Vaccines Debate

1 COVID-19 vaccination experiences 72 Johnson &Johnson Vaccine Efficacy

2 Modi and COVID-19 vaccination 73 My Body, My Choice: Abortion &Vaccine Mandates

3 Vaccine Mandates 74 Pfizer and Operation Warp Speed

4 Vaccine and mask hesitancy 75 Twitter Discussions on COVID-19 Vaccines

5 Vaccinated vs unvaccinated 76 SARS-CoV-2 Vaccines: Efficacy and Mutations

6 Canadian vaccine distribution 77 COVID Vaccination in Ireland

7 Vaccines: Cost and Affordability 78 Chickenpox and Shingrix Vaccine

8 Education during pandemic 79 Vaccine Misinformation Campaigns

9 Vaccine Experience 80 No HIV Vaccine

10 COVID vaccine skepticism 81 Vaccine Side Effects: Understanding the Risks

11 Pfizer and Moderna 82 Vaccine Rollout in Philippines and Thailand

12 Trump Vaccine Endorsement 83 COVID-19 Vaccination Certificates

13 Vaccine passport debate 84 Moderna Vaccine Doses

14 Biden’s Vaccine Mandate 85 Vaccine efficacy debate

15 Immunity and Vaccines 86 Family’s COVID-19 Vaccination Experiences

16 Testing with flu vaccination 87 Vaccine prioritization for NHS staff

17 Public Health &Sports 88 Public unrest against vaccination requirements

18 Breakthroughs in Vaccine 89 Vaccination Status

19 Public Perception of Chinese Vaccines 90 FDA Vaccine Approval

20 Anti-vax Conspiracy Theories 91 Novak Djokovic’s Vaccine Stance

21 Sport and Covid-19 Restrictions in Australia 92 Singapore Multi-Vaccine Approach

22 COVID-19 Vaccine Rollout for Children 93 New Zealand’s Covid Response

23 mRNA Vaccines and Public Opinion 94 Vaccines and Cell Reception

24 Religious Views on COVID-19 Vaccines 95 Personal medical autonomy

25 Sharing Useful Content 96 Pfizer vaccine for children

26 Appointment booking &availability 97 Ireland’s COVID-19 Vaccination Rollout

27 AstraZeneca vaccine efficacy 98 Boris Johnson’s COVID Response

28 Delta Variant and Vaccination Strategies 99 Germany’s Response to COVID-19 Vaccination

29 Feminist COVID Advocacy 100 Vaccine Appointment slots

30 Digital Vaccine Cards 101 USA Regional Vaccination Impact

31 EU-UK Vaccine Diplomacy Post-Brexit 102 Vaccine Policies for Bars and Restaurants

32 Preparing for vaccine side effects 103 Nigeria’s COVID-19 Vaccine Procurement

33 Sputnik V: Russia’s COVID-19 vaccine and its global impact 104 Comparing vaccine mandates to the Holocaust

34 Vaccine Mandates in Air Travel 105 COVID-19 Second Dose Administration

35 UK Vaccination Rollout Progress and Challenges 106 Vaccine efficacy: Pfizer vs. Oxford-AstraZeneca

36 Societal Chaos 107 Vaccine Storage and Transport Logistics

37 COVID-19 Lockdowns: Effectiveness and Consequences 108 Vaccination and Christmas Celebrations

38 Vaccine Effectiveness Against Omicron 109 Ableism &Anti-Vaccine Beliefs

39 Nutrition and immune support: before and after vaccine 110 COVID-19 Vaccination in France

40 DeSantis’ vaccine policies in Florida 111 Scott Morrison’s vaccine distribution

41 Racism and Vaccine Hesitancy 112 Vaccination and Border Control Policy during pandemic

42 Bill Gates’ Vaccine Philanthropy 113 Kyrie Irving’s Stance on Vaccines

43 Music, Vaccines, and the Road to Recovery 114 Fox News and Conspiracy Theories on Covid-19 vaccines

44 Monkeypox Vaccination Challenges 115 HPV vaccination awareness and promotion

45 Elderly Population and Vaccination 116 vaccine passports for international travel

46 Africa’s Reliance on Imported Vaccines 117 Vaccine Development: Challenges and Progress

47 Vaccination Disparity in West Bank: Jewish vs. Palestinian 118 Ebola Vaccine for COVID-19 Treatment

48 Healthcare Worker Vaccination Mandates 119 Ivermectin for COVID-19 treatment

49 NYC Vaccination Updates &Milestones 120 Smallpox eradication through vaccination

50 COVID-19 Vaccinations in Texas: Updates and Disparities 121 SARS Outbreak

51 Rabies Prevention for Pets: Vaccination and Care 122 Reopening After Vaccines: Accelerating Return to Normal

52 Age-Based Vaccination Prioritization in Arizona 123 Japan’s COVID-19 Response and Vaccine Rollout

53 Historical Polio Vaccination 124 Criticism of Tory’s COVID-19 Response

54 Democrats’ vs republican Beliefs to Vaccination 125 Quarantine fatigue and the vaccine rollout

55 Effects of COVID-19 Vaccine on Pregnant Women 126 COVID-19 Vaccination Conspiracy

56 J&J Vaccine Hesitancy 127 Vaccination and Political Polarization

57 Vaccine Dose Progression: First, Second, and Beyond 128 Cuba’s COVID-19 Vaccine Development

58 Addressing Vaccine Hesitancy and Misinformation During the Pandemic 129 Vaccine Hesitancy and Resistance during Second and Third Waves

59 “Fauci’s Views on Vaccines” 130 Needle, Vaccine, and Politics: Debates

60 Children’s COVID Vaccines Debate 131 Vaccine hesitancy in the livestock community

61 Vaccine-related Deaths Debate 132 COVID-19’s Impact on the Cruise Industry

62 COVID-19 Vaccination Decision: Taking the Shot or Not? 133 Obesity and COVID-19 Vaccine Effectiveness

63 Vaccine-Related Myocarditis Risk 134 Kenya’s COVID-19 Vaccination drive

64 Risk of Blood Clots in COVID Vaccines 135 Nordic Countries’ COVID-19 vaccination Response

65 Emerging Variants and Vaccine Efficacy 136 Cuomo vs. Trump: The Politics of COVID-19 Vaccine Rollout

66 Mandatory COVID-19 vaccine for military 137 Anti-vaccine Activism

67 Government Surveillance and Vaccines 138 Novavax COVID-19 Vaccine Trials in the UK and US

68 Vaccine Skepticism 139 COVID-19 treatments: Hydroxychloroquine and Bleach

69 Booster Shot Efficacy 140 Legal ID in Voting and Vaccine Verification

70 Achieving Herd Immunity 141 Pfizer Vaccine Side Effects

#	Topic	#	Topic
0	Vaccines and Public Perception.	71	Compulsory Vaccines Debate
1	COVID-19 vaccination experiences	72	Johnson &Johnson Vaccine Efficacy
2	Modi and COVID-19 vaccination	73	My Body, My Choice: Abortion &Vaccine Mandates
3	Vaccine Mandates	74	Pfizer and Operation Warp Speed
4	Vaccine and mask hesitancy	75	Twitter Discussions on COVID-19 Vaccines
5	Vaccinated vs unvaccinated	76	SARS-CoV-2 Vaccines: Efficacy and Mutations
6	Canadian vaccine distribution	77	COVID Vaccination in Ireland
7	Vaccines: Cost and Affordability	78	Chickenpox and Shingrix Vaccine
8	Education during pandemic	79	Vaccine Misinformation Campaigns
9	Vaccine Experience	80	No HIV Vaccine
10	COVID vaccine skepticism	81	Vaccine Side Effects: Understanding the Risks
11	Pfizer and Moderna	82	Vaccine Rollout in Philippines and Thailand
12	Trump Vaccine Endorsement	83	COVID-19 Vaccination Certificates
13	Vaccine passport debate	84	Moderna Vaccine Doses
14	Biden’s Vaccine Mandate	85	Vaccine efficacy debate
15	Immunity and Vaccines	86	Family’s COVID-19 Vaccination Experiences
16	Testing with flu vaccination	87	Vaccine prioritization for NHS staff
17	Public Health &Sports	88	Public unrest against vaccination requirements
18	Breakthroughs in Vaccine	89	Vaccination Status
19	Public Perception of Chinese Vaccines	90	FDA Vaccine Approval
20	Anti-vax Conspiracy Theories	91	Novak Djokovic’s Vaccine Stance
21	Sport and Covid-19 Restrictions in Australia	92	Singapore Multi-Vaccine Approach
22	COVID-19 Vaccine Rollout for Children	93	New Zealand’s Covid Response
23	mRNA Vaccines and Public Opinion	94	Vaccines and Cell Reception
24	Religious Views on COVID-19 Vaccines	95	Personal medical autonomy
25	Sharing Useful Content	96	Pfizer vaccine for children
26	Appointment booking &availability	97	Ireland’s COVID-19 Vaccination Rollout
27	AstraZeneca vaccine efficacy	98	Boris Johnson’s COVID Response
28	Delta Variant and Vaccination Strategies	99	Germany’s Response to COVID-19 Vaccination
29	Feminist COVID Advocacy	100	Vaccine Appointment slots
30	Digital Vaccine Cards	101	USA Regional Vaccination Impact
31	EU-UK Vaccine Diplomacy Post-Brexit	102	Vaccine Policies for Bars and Restaurants
32	Preparing for vaccine side effects	103	Nigeria’s COVID-19 Vaccine Procurement
33	Sputnik V: Russia’s COVID-19 vaccine and its global impact	104	Comparing vaccine mandates to the Holocaust
34	Vaccine Mandates in Air Travel	105	COVID-19 Second Dose Administration
35	UK Vaccination Rollout Progress and Challenges	106	Vaccine efficacy: Pfizer vs. Oxford-AstraZeneca
36	Societal Chaos	107	Vaccine Storage and Transport Logistics
37	COVID-19 Lockdowns: Effectiveness and Consequences	108	Vaccination and Christmas Celebrations
38	Vaccine Effectiveness Against Omicron	109	Ableism &Anti-Vaccine Beliefs
39	Nutrition and immune support: before and after vaccine	110	COVID-19 Vaccination in France
40	DeSantis’ vaccine policies in Florida	111	Scott Morrison’s vaccine distribution
41	Racism and Vaccine Hesitancy	112	Vaccination and Border Control Policy during pandemic
42	Bill Gates’ Vaccine Philanthropy	113	Kyrie Irving’s Stance on Vaccines
43	Music, Vaccines, and the Road to Recovery	114	Fox News and Conspiracy Theories on Covid-19 vaccines
44	Monkeypox Vaccination Challenges	115	HPV vaccination awareness and promotion
45	Elderly Population and Vaccination	116	vaccine passports for international travel
46	Africa’s Reliance on Imported Vaccines	117	Vaccine Development: Challenges and Progress
47	Vaccination Disparity in West Bank: Jewish vs. Palestinian	118	Ebola Vaccine for COVID-19 Treatment
48	Healthcare Worker Vaccination Mandates	119	Ivermectin for COVID-19 treatment
49	NYC Vaccination Updates &Milestones	120	Smallpox eradication through vaccination
50	COVID-19 Vaccinations in Texas: Updates and Disparities	121	SARS Outbreak
51	Rabies Prevention for Pets: Vaccination and Care	122	Reopening After Vaccines: Accelerating Return to Normal
52	Age-Based Vaccination Prioritization in Arizona	123	Japan’s COVID-19 Response and Vaccine Rollout
53	Historical Polio Vaccination	124	Criticism of Tory’s COVID-19 Response
54	Democrats’ vs republican Beliefs to Vaccination	125	Quarantine fatigue and the vaccine rollout
55	Effects of COVID-19 Vaccine on Pregnant Women	126	COVID-19 Vaccination Conspiracy
56	J&J Vaccine Hesitancy	127	Vaccination and Political Polarization
57	Vaccine Dose Progression: First, Second, and Beyond	128	Cuba’s COVID-19 Vaccine Development
58	Addressing Vaccine Hesitancy and Misinformation During the Pandemic	129	Vaccine Hesitancy and Resistance during Second and Third Waves
59	“Fauci’s Views on Vaccines”	130	Needle, Vaccine, and Politics: Debates
60	Children’s COVID Vaccines Debate	131	Vaccine hesitancy in the livestock community
61	Vaccine-related Deaths Debate	132	COVID-19’s Impact on the Cruise Industry
62	COVID-19 Vaccination Decision: Taking the Shot or Not?	133	Obesity and COVID-19 Vaccine Effectiveness
63	Vaccine-Related Myocarditis Risk	134	Kenya’s COVID-19 Vaccination drive
64	Risk of Blood Clots in COVID Vaccines	135	Nordic Countries’ COVID-19 vaccination Response
65	Emerging Variants and Vaccine Efficacy	136	Cuomo vs. Trump: The Politics of COVID-19 Vaccine Rollout
66	Mandatory COVID-19 vaccine for military	137	Anti-vaccine Activism
67	Government Surveillance and Vaccines	138	Novavax COVID-19 Vaccine Trials in the UK and US
68	Vaccine Skepticism	139	COVID-19 treatments: Hydroxychloroquine and Bleach
69	Booster Shot Efficacy	140	Legal ID in Voting and Vaccine Verification
70	Achieving Herd Immunity	141	Pfizer Vaccine Side Effects

Certain topics exhibit a high correlation. To explore this relationship, we utilized cosine similarity on topic embeddings and generated a similarity matrix (Fig. 4). We found dense-colored blocks in the similarity matrix for topics 0-9, indicating a strong correlation among these clusters. These topics include “Vaccines and Public Perception,” “COVID-19 Vaccination Experiences”, “Modi and COVID-19 Vaccination,” “Vaccine Mandates,” “Vaccine and Mask Hesitancy,” “Vaccinated vs. Unvaccinated,” “Canadian vaccine distribution”,“Vaccines: Cost and Affordability”, “Education during the pandemic”, “Vaccine Experience,” and “COVID vaccine skepticism”. Conversely, topics 77-141 demonstrate a different pattern. The similarity scores within this range are <0.4, indicating minimal correlation among the topic clusters (as depicted by light-coloured blocks in the similarity matrix). However, a few instances show a strong relationship despite the lower overall similarity scores. Notable pairs of strongly correlated topics include “Preparing for vaccine side effects” –“Pfizer Vaccine Side Effects”, “Vaccine Misinformation Campaigns” –“Anti-vaccine Activism,” “Vaccine passport debate” –“vaccine passports for international travel,” and “Appointment booking & availability” –“COVID-19 Vaccination Decision: Taking the Shot or Not?”.

Fig. 4

Cosine similarity matrix illustrating the relationship between generated topic clusters.

Next, we examined the topic distribution in the top 6 countries, depicted in Fig. 5. Each graph represents tweet distribution per topic per country. Notably, the United States had a presence in 81 topic clusters, followed by the United Kingdom (37), Canada (15), India (14), Australia (12), and South Africa (5). We also argue that a minimum topic size of 1000 is significant [26] in capturing topics as <1% of tweets are geotagged [21, 22, 32]. A comparative analysis in [24] showed that full-volume tweets and geotagged tweets exhibit similar volumetric patterns, implying that the identified topics are near-true representations of global events concerning COVID-19 vaccines and vaccination.

Fig. 5

The distributions of tweets across 142 topics over the top 6 countries in the discourse.

Further, our topical analysis reveals the presence of multiple (contextually) regional topic clusters that received discussions worldwide — some such topic clusters include “Modi and COVID-19 vaccination”, “Canadian vaccine distribution”, “Trump Vaccine Endorsement”, “Biden’s Vaccine Mandate”. For instance, the discussion surrounding “Modi and COVID-19 vaccination” (topic 2) gained attention worldwide and is only the (contextually) regional topic cluster (centred around India) that is present in the top 5 topics discussed worldwide. Although this topic is in the Indian context, the global interest may be due to the influence of the Indian diaspora spread worldwide. As a result, discussions related to COVID-19 vaccines and vaccination efforts gain traction and visibility across global communities, consequently expanding the topic’s reach beyond regional boundaries [14].

The discussion on COVID-19 Vaccination before and after the vaccination rollout and the rise in confirmed cases and deaths between 2020–2022 affected people in terms of physical and mental health [8]. Studies related to the pandemic and its sentiments have reported a rise in positive and negative sentiments [13, 19] in the early phases of the pandemic. Following up with the earlier studies in the literature, utilizing the GeoCovaxTweets dataset, we explored the early 2020 –late 2022 timeline of the COVID-19 pandemic through topic-involved sentiment analysis across the globe and the top 10 topics in the discourse. Results show that neutral sentiments dominated the discourse globally (shown in Fig. 6).

Fig. 6

The class-wise global daily distribution of the sentiments of the tweets.

Figure 7 explores the distributions of tweets across the top 10 topics discussed between January 2020 and November 2022. We only considered global topics for the sentiment analysis while ignoring regional topic clusters like “Modi and COVID-19 vaccination” and “Canadian vaccine distribution”. The top 10 topics include “Vaccines and Public Perception”, “COVID-19 vaccination experiences”, “Vaccine Mandates”, “Vaccine and mask hesitancy”, “Vaccinated vs unvaccinated”, “Vaccines: Cost and Affordability”,“Education during pandemic”, “Vaccine Experience”, “COVID vaccine skepticism” and “Pfizer and Moderna”. Our volumetric analysis reveals the increase in tweets regarding vaccines and vaccination between early 2021 and late 2021 for the top 10 topics. Next, we explored (in Table 5) the overall and monthly-basis sentiment proportions (positive/negative ratios) for that period across the 10 most discussed topics. We observed the highest average sentiment proportions for the topic “Pfizer and Moderna” (0.43) followed by “Vaccines and Public Perception” (0.40), “Vaccine Mandates” (0.36), “Education during pandemic” (0.36), “COVID vaccine skepticism” (0.36), “COVID-19 vaccination experiences” (0.33).

Fig. 7

The distributions of sentiments of top 10 topics between January 2020 and November 2022.

Table 5

Per-month basis sentiment proportions for the top 10 topics in the discourse

Topic Name	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Avg
Vaccines and Public Perception	0.46	0.54	0.65	0.50	0.53	0.47	0.18	0.19	0.18	0.41	0.63	0.53	0.40
COVID-19 vaccination experiences	0.48	0.53	0.57	0.46	0.51	0.44	0.20	0.19	0.19	0.17	0.22	0.13	0.33
Vaccine Mandates	0.51	0.62	0.57	0.48	0.55	0.49	0.23	0.22	0.16	0.20	0.31	0.18	0.36
Vaccine and mask hesitancy	0.46	0.55	0.56	0.53	0.49	0.46	0.18	0.21	0.14	0.14	0.11	0.07	0.28
Vaccinated vs unvaccinated	0.49	0.69	0.67	0.43	0.49	0.44	0.18	0.17	0.25	0.04	0.03	0.05	0.19
Vaccines: Cost and Affordability	0.49	0.34	0.94	0.41	0.39	0.56	0.19	0.20	0.17	0.08	0.04	0.09	0.31
Education during pandemic	0.39	0.57	0.68	0.49	0.71	0.35	0.25	0.23	0.14	0.20	0.09	0.14	0.36
Vaccine Experience	0.36	0.50	0.68	0.44	0.59	0.48	0.25	0.17	0.15	0.19	0.07	0.18	0.31
COVID vaccine skepticism	0.51	0.34	0.54	0.44	0.57	0.37	0.17	0.20	0.14	0.31	0.54	0.59	0.36
Pfizer and Moderna	0.70	0.52	0.72	0.46	0.52	0.43	0.23	0.17	0.15	0.52	0.50	0.22	0.43

Regarding “Vaccines and Public Perception,” the sentiment proportion ranges from 0.18 to 0.65. There’s an upward trend from January to March, followed by a decrease until May and a gradual increase until November. Concerning “COVID-19 vaccination experiences,” the proportion ranges from 0.13 to 0.57. There’s a dip in proportion from April to June, followed by a slight recovery in July and August. Regarding “Vaccine Mandates,” the proportion ranges from 0.16 to 0.62. There’s a peak in proportion in February, followed by a decline until May and a slight increase until November. Regarding “Vaccine and mask hesitancy,” the proportion ranges from 0.07 to 0.56. There’s a general increase in sentiment from January to March, followed by a decrease until June, and then a gradual decline until November. For “Vaccinated vs unvaccinated,” the proportion ranges from 0.03 to 0.69. There’s a significant peak in proportion in February, followed by a sharp decline until December. Regarding “Vaccines: Cost and Affordability,” the proportion ranges from 0.04 to 0.94. There’s a significant increase in proportion in March, followed by a decline until May, and then a relatively stable proportion until December. This topic exhibits a high variability range. Regarding “Education during the pandemic,” the proportion ranges from 0.09 to 0.71. There’s a peak in proportion in May, followed by a gradual decline until October, and a slight increase until December. Concerning “Vaccine Experience,” the proportion ranges from 0.07 to 0.68. There’s an initial increase in proportion from January to March, followed by a decrease until May, and then fluctuation with no clear trend until December. Regarding “COVID vaccine skepticism”, the proportion ranges from 0.14 to 0.59. There’s a peak in proportion in June, followed by a gradual decline until September, and then an increase until November. Regarding “Pfizer and Moderna,” the proportion ranges from 0.15 to 0.72. There’s a significant increase in proportion in March, followed by a decline until May, and then relatively stable sentiment until November.

In summary, the sentiment analysis shows that the sentiment proportion fluctuated throughout the year, suggesting that public perception and sentiment regarding these topics were dynamic and subject to change. Some topics exhibit consistent positive sentiment (e.g., “Pfizer and Moderna”, “Vaccines and Public Perception”), while others display more variability (e.g., “vaccinated vs unvaccinated”). Interestingly, it is observed that in the month of March, the sentiment proportions across multiple topics are the highest, indicating the presence of positive public perception during the early phase of vaccine distribution. Conversely, the discourse surrounding vaccines and vaccination during late 2021 carried more negative sentiments.

Next, we explored the top 10 topic-sentiment frequencies for the top 6 countries in the discourse. The results are summarized in Table 6. The frequency of topic-sentiment patterns differs among countries, indicating variations in public sentiment or discourse intensity. For instance, the United States generally exhibits higher frequencies than other countries, suggesting a potentially more active or extensive discourse on the given topics. Several sentiment patterns consistently emerge across multiple countries. Topics such as “Vaccines and Public Perception”, “COVID-19 vaccination experience”, “Vaccine Mandates”, “Vaccine and mask hesitancy”, “Vaccinated vs unvaccinated”, “Vaccines: Cost and Affordability”, “Vaccine Experience” tend to have neutral sentiment as the dominant frequency across all countries, indicating some commonality in the sentiment distribution for these topics globally. While neutral sentiment prevails, there are instances of negative sentiment in the dataset. Negative sentiment may arise from criticism, dissatisfaction, or unfavourable opinions associated with specific topics. Although less frequent than neutral sentiment, these instances provide insights into the presence of dissent or negative sentiment within the discussions. The differences in frequencies and sentiments across countries may reflect cultural, regional, or contextual variations. Public sentiment is influenced by various factors such as cultural norms, political climate, socioeconomic conditions, and media influence. Therefore, the patterns observed in the table can potentially reflect these contextual differences.

Table 6

Top 10 topic-sentiment frequencies for the top 6 countries in the discourse

Country	Label with frequency
United States	Vaccines and Public Perception/Neutral (92.2k), COVID-19 vaccination experiences/Neutral (51.6k), Modi and COVID-19 vaccination/Neutral (25.8k), Vaccine Mandates/Neutral (19.6k), Vaccine and mask hesitancy/Neutral (17.9k), Vaccinated vs unvaccinated/Neutral (16.5), Vaccines and Public Perception/Negative (13.7k), Education during pandemic/Neutral (8.1k), COVID-19 vaccination experiences/Negative (8.1k), Vaccines: Cost and Affordability/Neutral (7.6k)
United Kingdom	Vaccines and Public Perception/Neutral (28.9k), COVID-19 vaccination experiences/Neutral (15.2k), Modi and COVID-19 vaccination/Neutral (8.5k), Vaccine Mandates/Neutral (5.6k), Vaccinated vs unvaccinated/Neutral (5.1k), Vaccine and mask hesitancy/Neutral (4.8k), Vaccines and Public Perception/Negative (3.1k), Vaccines: Cost and Affordability/Neutral (2.2k), Canadian vaccine distribution/Neutral (2.2k), Vaccine Experience/Neutral (2.1k)
India	Vaccines and Public Perception/Neutral (18.2k), Modi and COVID-19 vaccination/Neutral (15.7k), COVID-19 vaccination experiences/Neutral (8.6k), Vaccine Mandates/Neutral (3.7k), Vaccine and mask hesitancy/Neutral (2.4k), Canadian vaccine distribution/Neutral (1.6k), Vaccines: Cost and Affordability/Neutral (1.5k), Education during pandemic/Neutral (1.4k), Vaccinated vs unvaccinated/Neutral (1.3k), Vaccine Experience/Neutral (1.1k)
Canada	Vaccines and Public Perception/Neutral (14.7k), COVID-19 vaccination experiences/Neutral (7.9k), Modi and COVID-19 vaccination/Neutral (4.4k), Canadian vaccine distribution/Neutral (4.3k), Vaccine Mandates/Neutral (2.8k), Vaccinated vs unvaccinated/Neutral (2.8k), Vaccine and mask hesitancy/Neutral (2.6k), Vaccines and Public Perception/Negative (1.7k), Education during pandemic/Neutral (1.3k), Vaccines: Cost and Affordability/Neutral (1.1k)
Australia	Vaccines and Public Perception/Neutral (6.7k), COVID-19 vaccination experiences/Neutral (3.5k), Modi and COVID-19 vaccination/Neutral (2.4k), Vaccine Mandates/Neutral (1.4k), Vaccinated vs unvaccinated/Neutral (1.2k), Vaccine and mask hesitancy/Neutral (1.1k), Vaccines and Public Perception/Negative (0.8k) Vaccines: Cost and Affordability/Neutral (0.5k), Vaccine Experience/Neutral (0.5k), COVID-19 vaccination experiences/Negative (0.5k)
South Africa	Vaccines and Public Perception/Neutral (4.7k), COVID-19 vaccination experiences/Neutral (2.1k), Modi and COVID-19 vaccination/Neutral (1.4k), Vaccine Mandates/Neutral (1.0k), Vaccinated vs unvaccinated/Neutral (0.7k), Vaccines and Public Perception/Negative (0.6k), Vaccine and mask hesitancy/Neutral (0.5k), Vaccines: Cost and Affordability/Neutral (0.3k), Education during pandemic/Neutral (0.3k), Canadian vaccine distribution/Neutral (0.3k)

4.3 Recreating the dataset

The GeoCovaxTweets dataset will be made publicly available from Harvard Dataverse after the peer review. We release the following tweet objects in the dataset: id, created_at, author.id, author.location, source, entities.hashtags, geo.country_code, author.public_metrics.followers_count, and state. Twitter’s data re-distribution policy⁴ restricts the sharing of raw Twitter data with third parties. Therefore, tweet identifiers (i.e., id tweet object) need to be hydrated to re-create the dataset locally. Hydration of tweet identifiers refers to the process of fetching raw tweet data using Twitter’s tweet lookup endpoint. The lookup endpoint has a limit of 900 requests per 15-minute window; therefore, GeoCovaxTweets can be re-created locally within a few hours.

Tools such as Hydrator (desktop application) and twarc (Python library) can be used to hydrate the tweet identifiers present in GeoCovaxTweets. In both use cases—hydrating all the tweet identifiers in GeoCovaxTweets or a subset—tweet identifiers should be in a file (e.g., TXT/CSV) with each identifier on a different line without any header and quotes. With the Hydrator app, we need to link a Twitter account for authorization and load the file with tweet identifiers for starting hydration. In the case of twarc, we can use the following command with valid Twitter API credentials:

twarc2 hydrate –consumer-key

consumer-key-here –consumer-secret

consumer-secret-here –access-token

access-token-here –access-token-secret

access-token-secret-here ids-file.txt data.jsonl

The above command hydrates the tweet identifiers in ids-file.txt and saves the API responses (hydrated data) in data.jsonl. Such hydrated data can be converted to CSV⁵ for loading the data as pandas dataframe for convenience.

4.4 Dataset usages

GeoCovaxTweets covers a comprehensive conversational dynamics of support, criticism, and hesitance towards the COVID-19 vaccines and vaccination on Twitter. There are several applications and potential usages of GeoCovaxTweets. We discuss some of them below:

–
Spatio-temporal analyses: The dataset can assist in examining how anti-vaccine discussions evolve spatially and tracking temporal relationships between vaccine intakes and social media discussions at the topical- and sentimental-levels. The geotagged tweets can be explored to study vaccine hesitancy at the district-, city-, state- or country-levels for hot-spot prediction and to get a precise view of how vaccine hesitancy impacts health outcomes. Although we provide state and country information, the dataset also contains tweets with point locations (i.e., geo coordinates) which can be filtered for performing spatial analyses at more granular levels than the state.
–
Location inference. Spatial analyses require geographical data, particularly the origin locations of tweets, rather than the locations mentioned in tweet texts. Dependence on toponyms for spatial analyses can generate biased results due to the Location A/B problem, as people in location A might participate in the discourse specific to location B. Instagram has been reported to generate the most COVID-19-related tweets geotagged with point coordinates, with the majority of them being throwback content, i.e., contexts from the past [22], concluding that all geotagged tweets from Instagram might not necessarily be originating from the respective geotagged sources. Since GeoCovaxTweets contains both types of geotagged tweets, i.e., point coordinates, and place information (bounding box), training models [22] for addressing the Location A/B problem can be another potential use case.
–
Downstream datasets: GeoCovaxTweets can be used to identify relevant tweets and generate labelled training datasets for fine-tuning language models for downstream tasks.
–
Network analysis: GeoCovaxTweets has application in network analysis, particularly for identifying key users or bots and their roles in spreading misinformation, hoaxes, and propaganda concerning COVID-19 vaccines and vaccination.
–
Correlation and causality: Correlation is a measure of computing the relationship between two variables. GeoCovaxTweets can be used to perform various types of correlation analysis—such as the correlation between hesitant-type tweets and the number of vaccinations over a period of time. Geotagged tweets have been reported to have variables that granger cause time series such as the daily confirmed COVID-19 cases [24]. GeoCovaxTweets can be explored if support-, criticize-, and hesitant-type tweets Granger-cause vaccination time series.
–
Dashboard: GeoCovaxTweets can also be used in web-based dashboards for visualizing situational data across different geographical contexts [7].

4.5 Limitations of the dataset

First, Twitter demographics are biased toward the younger generation and tech-aware users. Second, the dataset only includes English-language COVID-19 vaccine discourse missing the opinion of minority dialects and multilingual speakers [16]. Third, geotagged tweets are required to extract situational awareness as they involve spatial analyses; however, today, less than 1% of tweets are geotagged [21]. Hence, our dataset may not fully reflect the Twitter users’ conversations around the COVID-19 vaccines and vaccination.

Disclaimers

This dataset should be used only for non-commercial purposes while strictly adhering to Twitter’s policies. Note that the number of tweets after hydration can be less than reported, as tweets can be deleted or made private. GeoCovaxTweets has not been scrutinized for misinformative and propaganda tweets—the dataset contains all the tweets returned by Twitter’s Full-archive search endpoint for applied query+condition. Any location information present in the dataset and the geoinformation generated after hydration should be used while maintaining the privacy of the individuals.

5 Conclusion

This paper introduces the GeoCovaxTweets dataset, a comprehensive collection of geotagged tweets worldwide focused on COVID-19 vaccines and vaccination. The dataset consists of over 1.8 million English tweets from 464.9k users across 233 countries and territories, spanning from January 01, 2020, to November 25, 2022. It captures diverse conversations on COVID-19 vaccines across multiple thematic areas.

Using topic clustering, we identified 142 topics with a minimum of 1000 tweets each. These topics cover various subjects, including vaccines and public perception, COVID-19 vaccination experiences, and vaccine mandates. Analyzing topic volumes in the top 6 represented countries, we observed the United States as the leader in topic discussions, followed by the United Kingdom, Canada, India, Australia, and South Africa. This highlights the need to address vaccine hesitancy, equity, access, and mandates globally, given the complex and diverse viewpoints surrounding COVID-19 vaccines. Sentiment analysis revealed a predominance of neutral sentiments in the global discourse on COVID-19 vaccination, with positive and negative sentiments also present. Topics such as “Vaccines and Public Perception” and “COVID-19 Vaccination Experiences” displayed a higher occurrence of positive, negative, and neutral sentiments compared to others. Overall, the GeoCovaxTweets dataset offers valuable insights for crisis computing researchers, enabling exploration of the conversational dynamics of COVID-19 vaccines across various spatial and temporal dimensions. The dataset’s extensive coverage and geotagged information facilitate trend analysis, opinion shifts, identification of misinformation, and monitoring of anti-vaccination campaigns.

Footnotes

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