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


ABSTRACT: INTRODUCTION:Cervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized. METHODS: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. RESULTS: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. DISCUSSION: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. CONCLUSIONS:Future studies using social media data should consider sampling methods to uncover a wider breadth of conversations about health on social media.

SUBMITTER: Le GM 

PROVIDER: S-EPMC6370210 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Le Gem M GM   Radcliffe Kate K   Lyles Courtney C   Lyson Helena C HC   Wallace Byron B   Sawaya George G   Pasick Rena R   Centola Damon D   Sarkar Urmimala U  

PloS one 20190211 2


<h4>Introduction</h4>Cervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized.<h4>Methods</h4>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 th  ...[more]

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