Literature DB >> 26036946

Exploring the presentation of HPV information online: A semantic network analysis of websites.

Jeanette B Ruiz1, George A Barnett2.   

Abstract

CONTEXT: Negative vaccination-related information online leads some to opt out of recommended vaccinations.
OBJECTIVE: To determine how HPV vaccine information is presented online and what concepts co-occur.
METHODS: A semantic network analysis of the words in first-page Google search results was conducted using three negative, three neutral, and three positive search terms for 10 base concepts such as HPV vaccine, and HPV immunizations. In total, 223 of the 300 websites retrieved met inclusion requirements. Website information was analyzed using network statistics to determine what words most frequently appear, which words co-occur, and the sentiment of the words.
RESULTS: High levels of word interconnectivity were found suggesting a rich set of semantic links and a very integrated set of concepts. Limited number of words held centrality indicating limited concept prominence. This dense network signifies concepts that are well connected. Negative words were most prevalent and were associated with describing the HPV vaccine's side-effects as well as the negative effects of HPV and cervical cancer. A smaller cluster focuses on reporting negative vaccine side-effects. Clustering shows the words women and girls closely located to the words sexually, virus, and infection. DISCUSSION: Information about the HPV vaccine online centered on a limited number of concepts. HPV vaccine benefits as well as the risks of HPV, including severity and susceptibility, were centrally presented. Word cluster results imply that HPV vaccine information for women and girls is discussed in more sexual terms than for men and boys.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Concept co-occurrence; HPV vaccine websites; Semantic network analysis

Mesh:

Substances:

Year:  2015        PMID: 26036946     DOI: 10.1016/j.vaccine.2015.05.017

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  4 in total

1.  Semantic network analysis of vaccine sentiment in online social media.

Authors:  Gloria J Kang; Sinclair R Ewing-Nelson; Lauren Mackey; James T Schlitt; Achla Marathe; Kaja M Abbas; Samarth Swarup
Journal:  Vaccine       Date:  2017-05-27       Impact factor: 3.641

2.  Semantic Network Analysis Reveals Opposing Online Representations of the Search Term "GMO".

Authors:  Ke Jiang; Brittany N Anderton; Pamela C Ronald; George A Barnett
Journal:  Glob Chall       Date:  2017-12-27

3.  [A Text Mining Analysis of HPV Vaccination Research Trends].

Authors:  Yedong Son; Hee Sun Kang
Journal:  Child Health Nurs Res       Date:  2019-10-31

4.  Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

Authors:  Dexia Kong; Anfan Chen; Jingwen Zhang; Xiaoling Xiang; W Q Vivian Lou; Timothy Kwok; Bei Wu
Journal:  J Med Internet Res       Date:  2022-09-02       Impact factor: 7.076

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.