ArticlePloS one2019
Perceptions of cervical cancer prevention on Twitter uncovered by different sampling strategies.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.Journal of medical Internet research · 2025Article
- The Role of Internet Information on Anti-HPV Vaccines: A Comprehensive Overview of a Double-Edged Sword.Vaccines · 2025Review
- Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study.JMIR infodemiology · 2024Article
- Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data.JMIR cancer · 2023Article
- HPV vaccine narratives on Twitter during the COVID-19 pandemic: a social network, thematic, and sentiment analysis.BMC public health · 2023Article
- The social amplification and attenuation of COVID-19 risk perception shaping mask wearing behavior: A longitudinal twitter analysis.PloS one · 2021Article
- Understanding Human Papillomavirus Vaccine Promotions and Hesitancy in Northern California Through Examining Public Facebook Pages and Groups.Frontiers in digital health · 2021Article
- What people share about the COVID-19 outbreak on Twitter? An exploratory analysis.BMJ health & care informatics · 2020Article
- Sentiment Analysis Methods for HPV VaccinesRelated Tweets Based on Transfer Learning.Healthcare (Basel, Switzerland) · 2020Article
- A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017).Human vaccines & immunotherapeutics · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
introductionCervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized.
methodsWe 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.
resultsAdvocacy-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.
conclusionsFuture studies using social media data should consider sampling methods to uncover a wider breadth of conversations about health on social media.
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