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

JMIR Theme Issue: COVID-19 Special Issue (2452) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infodemiology and Infoveillance (1349) Natural Language Processing (692) Machine Learning (1465) Discourse (38)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41319, first published .
Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Authors of this article:

Gabriel Lindelöf1, 2 Author Orcid Image ;   Talayeh Aledavood1 Author Orcid Image ;   Barbara Keller1 Author Orcid Image
Article Authors Cited by (10) Tweetations (21) Metrics

Gabriel Lindelöf   1, 2 , MSc ;   Talayeh Aledavood   1 , PhD ;   Barbara Keller   1 , PhD

1 Department of Computer Science, Aalto University, Espoo, Finland

2 Department of Management and Engineering, Linköping University, Linköping, Sweden

Corresponding Author:

  • Gabriel Lindelöf, MSc
  • Department of Computer Science
  • Aalto University
  • P.O. Box 11000 (Otakaari 1B)
  • FI-00076 AALTO
  • Espoo, 02150
  • Finland
  • Phone: 358 9 47001
  • Email: gabriel.lindeloef@gmail.com