Twitter users are spontaneously forming tribe-like communities of like-minded people who even share their own distinct languages, new research has found.
Scientists from Royal Holloway, University of London, and Princeton University in New Jersey found they could use the language in Tweets to group users into communities with a common character, occupation or interest.
They suggest that the use of a common language could allow members of such ‘Twitter tribes’ to quickly identify like-minded users, and that further investigation could yield insights into how sub-cultures evolve online.
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 publicly available messages sent via Twitter, which meant that they could record conversations between two or many participants.
The study, recently published in the open-acess journal EPJ Data Science, describes how the researchers analysed 75 million tweets sent by 189,000 users. 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 same community.
‘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.’
In the paper, the researchers write:
Online social networks offer us an unprecedented opportunity to systematically study the large-scale structure of human interactions. Our approach suggests that groups with distinctive cultural characteristics or common interests can be discovered by identifying communities in interaction networks purely on the basis of topological structure.
This approach has several benefits when compared to surveying groups identified on a smaller scale: it is systematic, and groups are identified and classified in an unbiased way; when applied to online social networks it is non-intrusive; and it easily makes use a large volume of rich data.
The potential to include social group identification, customise online experience, and crowd-source characterisation could certainly be applied to healthcare marketing, public health campaigns, etc. As the report points out, having people with a negative attitude towards vaccination preferentially in contact with those of the same opinion could lead to clusters of susceptibles and increased risk of outbreaks. So any process that structures people into groups could potentially play a strong role in cultural evolution, as well as in the spread of information or pathogens.