Perceptions of cervical cancer prevention on Twitter uncovered by different sampling strategies.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2019
Historique:
received: 11 05 2018
accepted: 21 01 2019
entrez: 12 2 2019
pubmed: 12 2 2019
medline: 5 11 2019
Statut: epublish

Résumé

Cervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized. We obtained publicly-available Twitter data from 2014 using three sampling strategies (top-ranked, simple random sample, and topic model) based on key words related to cervical cancer prevention. We conducted a content analysis of 100 tweets from each of the three samples and examined the extent to which the narratives and frequency of themes differed across samples. Advocacy-related tweets constituted the most prevalent theme to emerge across all three sample types, and were most frequently found in the top-ranked sample. A random sample detected the same themes as topic modeling, but the relative frequency of themes identified from topic modeling fell in-between top-ranked and random samples. Variations in themes uncovered by different sampling methods suggest it is useful to qualitatively assess the relative frequency of themes to better understand the breadth and depth of social media conversations about health. Future studies using social media data should consider sampling methods to uncover a wider breadth of conversations about health on social media.

Identifiants

pubmed: 30742683
doi: 10.1371/journal.pone.0211931
pii: PONE-D-18-11732
pmc: PMC6370210
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0211931

Subventions

Organisme : NCI NIH HHS
ID : R01 CA178875
Pays : United States

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Gem M Le (GM)

UCSF Center for Vulnerable Populations, Division of General Internal Medicine, Department of Medicine, UCSF, San Francisco, CA, United States of America.

Kate Radcliffe (K)

UCSF Center for Vulnerable Populations, Division of General Internal Medicine, Department of Medicine, UCSF, San Francisco, CA, United States of America.

Courtney Lyles (C)

UCSF Center for Vulnerable Populations, Division of General Internal Medicine, Department of Medicine, UCSF, San Francisco, CA, United States of America.

Helena C Lyson (HC)

UCSF Center for Vulnerable Populations, Division of General Internal Medicine, Department of Medicine, UCSF, San Francisco, CA, United States of America.

Byron Wallace (B)

College of Computer and Information Science, Northeastern University, Boston, MA, United States of America.

George Sawaya (G)

Department of Obstetrics and Gynecology, UCSF, San Francisco, CA, United States of America.

Rena Pasick (R)

Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, United States of America.

Damon Centola (D)

Annenberg School of Communications, University of Pennsylvania, Philadelphia, PA, United States of America.

Urmimala Sarkar (U)

UCSF Center for Vulnerable Populations, Division of General Internal Medicine, Department of Medicine, UCSF, San Francisco, CA, United States of America.

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Classifications MeSH