Analyse de réseau sémantique du sentiment de vaccination dans les médias sociaux en ligne

Aperçu: G.M.

Le but de l'étude est d'examiner le sentiment actuel sur la vaccination sur les réseaux sociaux en construisant et en analysant des réseaux sémantiques d'informations sur les vaccins provenant de sites Web fortement partagés d'utilisateurs de Twitter aux États-Unis; Et pour aider à la communication de la santé publique des vaccins. 

L'hésitation au sujet des vaccins continue de contribuer à la couverture vaccinale sous-optimale aux États-Unis, ce qui présente un risque important d'épidémies, mais reste mal compris.

Le réseau de sentiments positifs s'est centré sur les parents et s'est concentré sur la communication des risques et des avantages pour la santé, mettant en évidence des concepts médicaux tels que la rougeole, l'autisme, le vaccin contre le VPH, le lien vaccin-autisme, la méningococcie et le vaccin ROR.  

En revanche, le réseau négatif s'est centré sur les enfants et s'est concentré sur les organismes tels que les CDC, l'industrie des vaccins, les médecins, les grands médias, les sociétés pharmaceutiques et les États-Unis.  

La prévalence du sentiment de vaccin négatif s'est manifestée grâce à des messages divers, encadrés par le scepticisme et la méfiance envers les organisations gouvernementales qui communiquent des preuves scientifiques favorisant les avantages positifs pour le vaccin. 

Vaccine. 2017 Jun 22;35(29):3621-3638. doi: 10.1016/j.vaccine.2017.05.052. Epub 2017 May 27.

Semantic network analysis of vaccine sentiment in online social media

Kang GJ¹, Ewing-Nelson SR², Mackey L², Schlitt JT¹, Marathe A², Abbas KM³, Swarup S⁴.

Author information

1

Department of Population Health Sciences, Virginia Tech, USA; Biocomplexity Institute, Virginia Tech, USA.

2

Biocomplexity Institute, Virginia Tech, USA.

3

Department of Population Health Sciences, Virginia Tech, USA.

4

Biocomplexity Institute, Virginia Tech, USA. Electronic address: swarup@vt.edu.

Abstract

OBJECTIVE:

To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.

BACKGROUND:

Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.

METHODS:

We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.

RESULTS:

The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.

CONCLUSION:

Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.

Copyright © 2017. Published by Elsevier Ltd.

PMID: 28554500

DOI: 10.1016/j.vaccine.2017.05.052