{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T11:29:39Z","timestamp":1789212579862,"version":"build-2803163510"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, <jats:italic>P<\/jats:italic> &amp;lt; 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (<jats:italic>P<\/jats:italic> &amp;lt; 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (<jats:italic>P<\/jats:italic> = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (<jats:italic>P<\/jats:italic> &amp;lt; 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (<jats:italic>P<\/jats:italic> = 0.003), (<jats:italic>P<\/jats:italic> = 0.002), and (<jats:italic>P<\/jats:italic> &amp;lt; 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community\u2013based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.<\/jats:p>","DOI":"10.3389\/fpubh.2022.987376","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T07:02:15Z","timestamp":1660287735000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts"],"prefix":"10.3389","volume":"10","author":[{"given":"Blessing","family":"Ogbuokiri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Ahmadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola Luigi","family":"Bragazzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Movahedi Nia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruce","family":"Mellado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Orbinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Asgary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jude","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"B1","unstructured":"Coronavirus (COVID-19) Update: FDA Authorizes First Oral Antiviral for Treatment of COVID-192021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.japh.2021.05.009","article-title":"COVID-19 and influenza vaccine hesitancy among college students","volume":"61","author":"Silva","year":"2021","journal-title":"J Am Pharm Assoc"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e06200","DOI":"10.1016\/j.heliyon.2021.e06200","article-title":"Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data","volume":"7","author":"Rahman","year":"2021","journal-title":"Heliyon"},{"key":"B4","unstructured":"COVID-19 Vaccines2020"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2586","DOI":"10.1080\/21645515.2020.1780846","article-title":"Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases","volume":"16","author":"Puri","year":"2020","journal-title":"Hum Vaccines Immunother"},{"key":"B6","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1136\/postgradmedj-2021-140685","article-title":"Using Twitter for sentiment analysis towards AstraZeneca\/Oxford, Pfizer\/BioNTech and Moderna COVID-19 vaccines","volume":"98","author":"Marcec","year":"2021","journal-title":"Postgrad Med J"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e25734","DOI":"10.2196\/25734","article-title":"Quantifying the influence of delay in opinion transmission of COVID-19 information propagation: modeling study","volume":"21","author":"Yin","year":"2021","journal-title":"J Med Intern Res"},{"key":"B8","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1080\/14760584.2022.2023355","article-title":"COVID-19 vaccine acceptance and hesitancy among healthcare workers in South Africa","volume":"21","author":"Wiysonge","year":"2022","journal-title":"Expert Rev Vaccines"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/21645515.2021.1893062","article-title":"Vaccine hesitancy in the era of COVID-19: could lessons from the past help in divining the future?","volume":"18","author":"Wiysonge","year":"2022","journal-title":"Hum Vaccines Immunother"},{"key":"B10","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1080\/14760584.2021.1949291","article-title":"COVID-19 vaccine hesitancy in South Africa: how can we maximize uptake of COVID-19 vaccines?","volume":"20","author":"Cooper","year":"2022","journal-title":"Expert Rev Vaccines"},{"key":"B11","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1080\/21645515.2018.1460987","article-title":"Vaccine hesitancy - 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