This paper is in the following e-collection/theme issue:

Theme Issue: Medical Informatics and COVID-19 (104) JMIR Theme Issue: COVID-19 Special Issue (2394) Infodemiology and Infoveillance (1278) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Mass Media/Social Media Communication and Campaigns (450) Vaccination and Immunization in the Digital Age (347) COVID-19 Vaccination (128) Linguistics, Comprehension, and Learning (52)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22916, first published .
Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

Authors of this article:

Sefater Gbashi1 Author Orcid Image ;   Oluwafemi Ayodeji Adebo1 Author Orcid Image ;   Wesley Doorsamy2 Author Orcid Image ;   Patrick Berka Njobeh1 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. Hu T, Wang S, Luo W, Zhang M, Huang X, Yan Y, Liu R, Ly K, Kacker V, She B, Li Z. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. Journal of Medical Internet Research 2021;23(9):e30854 View
  2. Melton C, Olusanya O, Ammar N, Shaban-Nejad A. Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health 2021;14(10):1505 View
  3. Saini V, Liang L, Yang Y, Le H, Wu C. The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR Infodemiology 2022;2(1):e37077 View
  4. Kumar S, Badruddin Khan M, Hoque Abul Hasanat M, Khader Jilani Saudagar A, AlTameem A, AlKhathami M. Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis. Computers, Materials & Continua 2023;74(1):897 View
  5. Li T, Zeng Z, Sun J, Sun S. Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media. The Electronic Library 2022;40(4):435 View
  6. Christensen B, Laydon D, Chelkowski T, Jemielniak D, Vollmer M, Bhatt S, Krawczyk K. Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study. JMIR Infodemiology 2022;2(2):e35121 View
  7. Zhu J, Weng F, Zhuang M, Lu X, Tan X, Lin S, Zhang R. Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model. International Journal of Environmental Research and Public Health 2022;19(20):13248 View
  8. Aljedaani W, Saad E, Rustam F, de la Torre Díez I, Ashraf I. Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends. Mathematics 2022;10(17):3199 View
  9. Hove C, Cilliers L. A structured literature review of the health infodemic on social media in Africa. Jàmbá Journal of Disaster Risk Studies 2023;15(1) View
  10. Arya V, Mishra A, González-Briones A. Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2023;11(4):507 View
  11. Gbashi S, Njobeh P. Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review. Applied Sciences 2024;14(8):3421 View

Books/Policy Documents

  1. Paliwal S, Parveen S, Afshar Alam M, Ahmed J. ICT Systems and Sustainability. View