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Open Access

Retraction

Retraction: Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach

  • Shantanu Dutta,
  • Ashok Kumar,
  • Moumita Dutta,
  • Caolan Walsh
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After this article [1] was published, concerns came to light regarding data use permissions. The authors obtained news articles for this study on Factiva. While the authors represented to PLOS that they had legitimate permissions to access the articles, concerns were noted post-publication that the authors’ data mining of news articles on Factiva did not comply with the terms of the University of Ottawa’s license with Factiva. Therefore, the authors retract this article.

The authors requested that PLOS remove the article from online publication, and informed PLOS that the University of Ottawa library and representatives of Factiva (Dow Jones) had both requested this action. Following an internal assessment of this case, PLOS agreed to remove the article from the PLOS ONE website at the time of retraction.

All authors agreed with retraction.

Reference

  1. 1. Dutta S, Kumar A, Dutta M, Walsh C (2021) Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach. PLoS ONE 16(6): e0252332. pmid:34077467

Citation: Dutta S, Kumar A, Dutta M, Walsh C (2021) Retraction: Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach. PLoS ONE 16(7): e0255347. https://doi.org/10.1371/journal.pone.0255347

Published: July 22, 2021

Copyright: © 2021 Dutta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.