that claims the ability to predict a communities’ risk of heart disease
from the language most commonly used by that community on twitter.
Analysing 148 million tweets, the researchers from the University of
Pennsylvania and the University of Melbourne mapped the tweeters to
counties in the US for which they also had data about the incidence of
heart disease and other health and demographic data. The researchers
found that they got a higher correlation between the language used in
tweets from people in those counties and heart disease than the
correlation between heart disease and other indicators such as income
and education, smoking, chronic disease and racial background.
The language that signalled the increased likelihood of heart disease
was broken down into 3 different categories of hostility/aggression,
hate/interpersonal tension and boredom/fatigue. Words that were
frequently used within those categories included words such as “fuck”,
“hate”, “drama”, “bitches” in the hostility/hate categories through to
“tired” and “shower” in the boredom category.
People tweeting in areas that had a lower risk of heart disease on
the other hand tended to use words such as “conference”, “student”,
“holiday” and “faith”.
The mainstream press, encouraged by the researchers involved in the
study, have picked up on this study as another example of how “big data”
and social media can reveal a whole range of information about a
population’s health and well-being. One report
featured the graphic from the study that showed the actual incidence of
heart disease in a part of the US with that predicted by Twitter. Tweets and heart disease The interesting thing about the graphic is that if you actually
compare the specific areas in each of the two diagrams, they are not
particularly the same at all.
The graphic is a great example of how data visualisation can be used to mislead
the viewer. The two diagrams seem similar because they use the same
colours and have identical areas which have to be the same because no
data is available.
Pick any two identical blocks however and the chances are that they
won’t be the same colour. This is because the predictive power of the
twitter analysis is actually quite low. Granted, the analysis may be slightly
better than using a combination of other indicators such as income,
education, chronic disease and race, but that difference is in actual
fact minuscule.
So what is the general language on Twitter actually saying? Well, it
is likely that the language used in tweets may reflect the
socio-economic level of a particular area. But even this claim is making
a number of unsubstantiated assumptions. It is easy to suppose that
lower socioeconomic populations would tweet more frequently about things
they hate using swear words than people who are more likely to be
educated from higher socioeconomic backgrounds. Interestingly however,
the study claims to have “controlled” for socio-economic status, which
means that the researchers believed that the language used was not
simply a representation of this status. Unfortunately, no details are
given of how they did this and so it is not possible to say whether this
was actually a reasonable assumption.
The study is interesting in that it suggests that a specific
communities’ makeup can be identified by the language that they use on
social media. This in turn may be an indicator of other factors such as
the specific community’s health and well-being and the consequences of
that health and well-being.
However, as attractive as those suggestions may be, the study showed
weak correlations and gave very little underlying support for any
particular theory of “causation”. So as much as they could speculate,
the researchers could really not say why they saw these statistical
results. The paper actually highlights some of the fundamental problems
with so-called big data where large numbers distort statistics to point at imagined relationships.
What this study definitely does not say is that being angry on
twitter will lead to, or is anyway related to, heart disease, which has
been the unfortunate suggestion in the selling of the study in news
reports.
