Twitter and Disease

Social media have been in the news lately as they have been used to spread the word about revolutions and demonstrations. According to a study in PLoS Computational Biology. Twitter is now being used to spread disease.

From the Author’s Summary

Sentiments about vaccination can strongly affect individual vaccination decisions. Measuring such sentiments – and how they are distributed in a population – is typically a difficult and resource-intensive endeavor. We use publicly available data from Twitter, a popular online social media service, to measure the evolution and distribution of sentiments towards the novel influenza A(H1N1) vaccine during the second half of 2009, i.e. the fall wave of the H1N1 (swine flu) pandemic. We find that projected vaccination rates based on sentiments expressed on Twitter are in very good agreement with vaccination rates estimated by the CDC with traditional phone surveys. Looking at the online social network, we find that both negative and positive opinions are clustered, and that an equivalent level of clustering of vaccinations in a population would strongly increase disease outbreak risks.

The paper is available online so details on the study design and data analysis are freely available.

This analysis can be seen as both a problem and its solution. In the Internet age, misinformation about health care and vaccinations has become widespread. People with genuine medical questions are directed to questionable sources  such as Generation Rescue, Joe Mercola’s sire, or any number of homoeopathic or naturopathic quackery sites. Many of these promote unfounded fears about vaccines amd recommend against them. This, in turn, leads top outbreaks of preventable diseases.

What the researchers found was that an increase in Twitter traffic related to anti-vaccine traffic was correlated strongly with a decrease in vaccinations. This could be due to a cluster of people who are already anti-vaccine, or it could indicate an element of persuasion. If the latter, the diseases will spread wider, spreading contagion via Twitter. Therein lies the problem.

The solution is here as well. An on-going analysis of Twitter comments on a particular topic, could lead to an increased education focus in these particular areas. It won’t stop the contagion, but it might help.

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