2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

A Semantic Web Platform for Online Vaccine Sentiment 
Surveillance
Arash Shaban-Nejad*1, 2, Sonia Menon2 and David Buckeridge2

1University of California, Berkeley, CA, USA; 2Clinical and Health Informatics Research Group, McGill University, Montreal, QC, 
Canada

Objective
This paper presents our approach on design and development of 

an integrated semantic platform to capture the domain knowledge 
on vaccine sentiments, beliefs, and behaviours using ontologies. 
The vaccine sentiment ontology (VASON) provides more structure 
around the vast amount of unstructured data scattered over blog posts 
to facilitate blog content analysis, and discovering patterns of words 
or phrases in blogs text (e.g. specifying topics, themes, sentiment, 
beliefs and so on). It also assists in revealing opinionated claims 
and assertions in blogs and specifying the authors, forms, functions, 
geographical locations, audiences of blogs, as well as bloggers’ 
motives.

Introduction
Real-time monitoring and analysis of vaccine concerns over time and 

location could help immunisation programmes to tailor more effective 
and timely strategies to address specific public health concerns. 
In recent years attempts [1, 2] are being made to develop a more 
systematic monitoring of broader public vaccine concerns resulting in 
vaccine refusals and potential disease outbreaks. Automated sentiment 
analysis software applications are being developed to detect and track 
the emergence and spread, geographically and temporally, of online 
social media reports on vaccines by developing a new application for 
opinion mining and sentiment analysis. Although many of the current 
approaches for automated sentiment analysis provide a timely method 
to assess the sentiment of a population towards vaccination, they do 
not assess beliefs, perceptions and behaviours. Incorporating semantic 
approach by using ontologies captures the domain knowledge and 
supports automated extraction and analysis of text in blog posts 
related to vaccination.

Methods
The Vaccine Attitude Surveillance using Semantic Analysis 

(VASSA) Framework [3] is intended to support the automated 
extraction and analysis of text in blog posts related to vaccination. 
The framework consists of a Natural Language Processing (NLP) 
module, which is used for text analysis and concept extraction, and 
the VASON Ontology, which models existing knowledge about the 
relationships between vaccination beliefs and behaviours. VASON is 
operating at the overlapping borders between three kingdoms, namely 
Health, Online social networks, and Human behavioural model. The 
health part deals with concepts such as vaccine, vaccination, disease, 
health states, and adverse event. Concepts such as blogs, blog posts, 
bloggers, comments, forum, web sites and media are grouped under 
social networking category, while we study human perception, 
awareness, belief, behaviour and sentiment under the behavioural 
classification. The interaction between these three major components 
are analysed with the human factor at the centre at both individual 
and population levels. The data for creating the VASON conceptual 
model comes from the literature, databases and some of the existing 
vocabularies and biomedical ontologies.

Results
The VASON ontology can be used to facilitate concept extraction 

and analysis of the extracted concepts using an NLP module. The 
development of the ontology is currently in progress and we are 
now performing several experiments for our text extraction using 
the VASON sub-taxonomies adapted/imported from the Vaccine 
Ontology(VO) and the Disease Ontology.

Conclusions
VASON aims to provide knowledge on the factors driving vaccine 

refusal and to identify potential interventions for increasing coverage. 
The work on the ontology is in progress, and the effort on integrating 
the ontological knowledge with existing relevant public health 
resources and services is still underway. Also we are enriching the 
semantic model to perform more sophisticated queries on the relations 
between vaccine beliefs, vaccine adverse events, and the risk factors.

Keywords
Vaccine Sentiment Analysis; Online Social Media; Vaccine refusal; 
Semantic Web; Ontologies

Acknowledgments

The project is funded by the Public Health Agency/Canadian Institutes of 
Health Research Influenza Research Network (PCIRN).

References

1. Salathé M., and Khandelwal S.: Assessing Vaccination Sentiments with 
Online Social Media: Implications for Infectious Disease Dynamics 
and Control . PLoS Comput Biol. Oct 2011; 7(10): e1002199.

2. Larson, H.J., Smith D.M.,Paterson P, et al. Measuring Vaccine
Confidence. Lancet Infectious Diseases 2013; 13: 606 – 613. Infect
Dis 2013 . doi.org/10.1016/S1473-3099(13)70108-7

3. Brien S., Naderi N., Shaban-Nejad A., Mondor L., Kroemker D.,
Buckeridge DL.: Vaccine attitude surveillance using semantic
analysis: constructing a semantically annotated corpus. WWW
(Companion Volume), ACM Press, 2013: 683-686.

*Arash Shaban-Nejad
E-mail: arash.shaban-nejad@mcgill.ca    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 7(1):e157, 2015