Reactions to the opioid epidemic: A text-mining analysis of tweets.


Journal

Journal of addictive diseases
ISSN: 1545-0848
Titre abrégé: J Addict Dis
Pays: England
ID NLM: 9107051

Informations de publication

Date de publication:
Historique:
pubmed: 27 10 2020
medline: 5 10 2021
entrez: 26 10 2020
Statut: ppublish

Résumé

This study analyzed public reactions to the opioid epidemic using Twitter discourse.

Identifiants

pubmed: 33103608
doi: 10.1080/10550887.2020.1834812
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

183-188

Auteurs

Kavya Rajesh (K)

Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.

Gary Wilcox (G)

Center for Health Communication and Stan Richards School of Advertising and Public Relations, Moody College of Communication, The University of Texas at Austin, Austin, TX, USA; for Health Communication, Dell Medical School and Moody College of Communication and Stan Richards School of Advertising and Public Relations, The University of Texas at Austin, Austin, TX, USA.

David Ring (D)

Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.

Michael Mackert (M)

Center for Health Communication and Stan Richards School of Advertising and Public Relations, Moody College of Communication, The University of Texas at Austin, Austin, TX, USA; for Health Communication, Dell Medical School and Moody College of Communication and Stan Richards School of Advertising and Public Relations, The University of Texas at Austin, Austin, TX, USA.

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Classifications MeSH