Detecting discussion communities on vaccination in twitter

Abstract

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 immunization 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.