Do you belong to a Twitter cabal?

Researchers analyzed 75 million tweets and located several tight-knit communities that share a common lingo.

To examine the link between human language and social network structure, a team of mathematical biologists analyzed 75 million tweets from 250,000 users.

The researchers, led by Princeton’s Sebastian Funk, located several tight-knit communities (and subcommunities) that tweet far more heavily to each other than to the rest of Twitter and revealed a slew of communities in which members shared a common dialect, ScienceNOW reports.

Besides identifying social groups, the team says applications of their method include: customizing online experiences, targeted marketing, and crowd-source characterization.

Pictured above are some communities sharing a special lingo and some top-ranked words. (Rather than apply a label to these communities, the researchers identified them by numbers, which you can see here.)

Some examples of shared words in communities with more than 250 users:

  • Technology-minded teachers employed terms like: edublogs, edtech, elluminate, smartboard, wikispaces
  • One group focused on animal welfare used lots of puns: anipals, pawsome, furever, barktending
  • Fans of The Bieb often write: pleasee, <33 and Twilight fans write kstew, robsessed, twilighted
  • sxsw, tweetup, metrics, innovation, companies, data
  • playoff, bullpen, roster, offseason, postgame
  • aint, holla, chillin, mixtape, poppin, fasho
  • pastors, missional, worship, ministry
  • exxxotica, pornstar, adultcon
  • rubbish, reckon, blimey, gutted
  • pelosi, obamacare, libs, gop, acorn
  • College students in Milwaukee who frequent a particular coffee shop tweet: alterra, uwm, mke

By using each tweeter's unique set of words, the researchers predicted their chosen communities correctly about 80% of the time -- suggesting that words help tweeters identify themselves as community members.

The work was published in EPJ Data Science last month.

[Via ScienceNOW]

Image: Bryden et al. EPJ Data Science

This post was originally published on Smartplanet.com

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