A growing body of literature suggests that the data people make
public on the Web can be used to
track epidemics, predict box office hits and foretell other aspects of the future. Adding to this evidence, Vasileios Lampos, Tijl De
Bie and Nello Cristianini of the Intelligent Systems Laboratory at the
University of Bristol (UK) have released a paper
about the utility of Twitter for tracking flu outbreaks.
What’s
different about tapping social media instead of search queries, says senior author Nello Cristianini, is that individual tweets are
qualitatively different from search strings, which tend to be quite short.
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“There
is the potential in Twitter to understand the contents of the text, and isolate
specific self-diagnostic statements by the user, for example, “i am having
a headache,” says Cristianini.
Over
several months, the researchers were able to gather a database of over 50
million geo-located tweets which could then be compared to official data from
the U.K.’s national health service on flu incidence by region. By figuring out
which keywords in the database of tweets were associated with elevated levels
of flu, Lampos et al. were able to create a predictive model that transformed
keyword incidence in future tweets into a prediction of the severity of flu for
a given area.
This
flu-predicting signal from Twitter is an independent stream of information that
can “complement or improve the signal coming from search engine
queries,” says Cristianini.
Cristianini notes that all approaches that track self-reported symptoms suffer from the same bias, however: the more the media hypes a Flu epidemic, the more likely people are to go to their doctors (distorting the “official” numbers) and talk about suspicious symptoms on Twitter or other services.
Future
work might involve information from Facebook and other sources of status updates,
allowing researchers to become ever more adept at pinpointing outbreaks in
their earliest stages.
