Skip to main content

Detecting discussion communities on vaccination in twitter

Bello, Gema, Hernandez-Castro, Julio, Camacho, David (2016) Detecting discussion communities on vaccination in twitter. Future Generation Computer Systems, 66 . pp. 125-136. ISSN 0167-739X. (doi:10.1016/j.future.2016.06.032) (KAR id:58380)

PDF Author's Accepted Manuscript
Language: English
Click to download this file (1MB)
[thumbnail of final-FGCS-gema.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:


Vaccines have contributed to dramatically decrease mortality from infectious diseases in the 20th century.

However, several social discussion groups related to vaccines have emerged, influencing the opinion of

the population about vaccination for the past 20 years. These communities discussing on vaccines have

taken advantage of social media to effectively disseminate their theories. Nowadays, recent outbreaks

of preventable diseases such as measles, polio, or influenza, have shown the effect of a decrease in

vaccination rates. Social Networks are one of the most important sources of Big Data. Specifically,

Twitter generates over 400 million tweets every day. Data mining provides the necessary algorithms

and techniques to analyse massive data and to discover new knowledge. This work proposes the use of

these techniques to detect and track discussion communities on vaccination arising from Social Networks.

Firstly, a preliminary analysis using data from Twitter and official vaccination coverage rates is performed,

showing how vaccine opinions of Twitter users can influence over vaccination decision-making. Then,

algorithms for community detection are applied to discover user groups opining about vaccines. The

experimental results show that these techniques can be used to discover social discussion communities

providing useful information to improve immunisation strategies. Public Healthcare Organizations may

try to use the detection and tracking of these social communities to avoid or mitigate new outbreaks of

eradicated diseases

Item Type: Article
DOI/Identification number: 10.1016/j.future.2016.06.032
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Julio Hernandez Castro
Date Deposited: 03 Nov 2016 16:21 UTC
Last Modified: 08 Dec 2022 22:25 UTC
Resource URI: (The current URI for this page, for reference purposes)
Hernandez-Castro, Julio:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.