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

Infodemiology and Infoveillance (1369) JMIR Theme Issue: COVID-19 Special Issue (2461) Infectious Diseases (non-STD/STI) (1594) Vaccination and Immunization in the Digital Age (378) Natural Language Processing (708) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Infoveillance and Social Listening (214) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1667) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (159) Innovative Methods in Public Health and Surveillance (337) COVID-19 Vaccination (155)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30529, first published .
Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Authors of this article:

Andrzej Jarynowski1, 2 Author Orcid Image ;   Alexander Semenov3, 4 Author Orcid Image ;   Mikołaj Kamiński5 Author Orcid Image ;   Vitaly Belik1 Author Orcid Image
Article Authors Cited by (24) Tweetations (9) Metrics

Andrzej Jarynowski   1, 2 , DIS, MSc ;   Alexander Semenov   3, 4 , PhD ;   Mikołaj Kamiński   5 , MD ;   Vitaly Belik   1 , PhD

1 System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany

2 Interdisciplinary Research Institute, Wrocław/Głogów, Poland

3 Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States

4 Center for Econometrics and Business Analytics, St. Petersburg State University, Saint Petersburg, Russian Federation

5 Family Medicine Practice, Murowana Goślina, Poland

Corresponding Author:

  • Vitaly Belik, PhD
  • System Modeling Group
  • Institute for Veterinary Epidemiology and Biostatistics
  • Freie Universität Berlin
  • Königsweg 67
  • Berlin, 14163
  • Germany
  • Phone: 49 30 838 61129
  • Email: vitaly.belik@fu-berlin.de