A project led by scientists from Royal Holloway University
in collaboration with Princeton University, has found evidence of how people
form into tribe-like communities on social network sites such as Twitter.
In a paper published in EPJ
Data Science, they found that these communities have a common character,
occupation or interest and have developed their own distinctive languages.
"This means that by looking at the language someone
uses, it is possible to predict which community he or she is likely to belong
to, with up to 80% accuracy," said Dr John Bryden from the School of
Biological Sciences at Royal Holloway. "We searched for unusual words that
are used a lot by one community, but relatively infrequently by the others. For
example, one community often mentioned Justin Bieber, while another talked
about President Obama."
have different ‘languages’
Professor Vincent Jansen from Royal Holloway added:
"Interestingly, just as people have varying regional accents, we also
found that communities would misspell words in different ways. The Justin
Bieber fans have a habit of ending words in 'ee', as in 'pleasee', while school
teachers tend to use long words."
The team produced a map of the communities showing how they
have vocations, politics, ethnicities and hobbies in common. In order to do
this, they focused on the sending of publicly available messages via Twitter,
which meant that they could record conversations between two or many
To group these users into communities, they turned to
cutting-edge algorithms from physics and network science. The algorithms worked
by looking for individuals that tend to send messages to other members of the
Dr Bryden then suggested analysing the language use of these
Dr Sebastian Funk from Princeton University said: "When
we started to apply John's ideas, surprising groups started to emerge that we
weren't expecting. One 'anipals' group was interested in hosting parties to
raise funds for animal welfare, while another was a fascinating growing
community interested in the concept of gratitude."