Microbloggers may think they're interacting in one big
Twitterverse, but researchers at Carnegie Mellon
University's School of Computer Science find that regional
slang and dialects are as evident in tweets as they are in everyday
conversations.
Postings on Twitter -- “tweets” -- reflect some
well-known regionalisms, such as "y'all" (the South) and "yinz"
(Pittsburgh), plus the usual regional divides in references to
soda, pop and Coke.
Analyzing tweets
But Jacob Eisenstein, a post-doctoral fellow in CMU's Machine
Learning Department, said the automated method he and his
colleagues have developed for analyzing Twitter word use shows
regional dialects appear to be evolving within social media.
In northern California, something that's cool is "koo" in tweets,
while in southern California, it's "coo."
In many cities, something is "sumthin," but tweets in New York City
favor "suttin."
The "af" is an acronym that, like many others on Twitter, stands for a vulgarity. (Think the “f-word.“)
“LOL” is a commonly used acronym for "laughing out loud," but Twitterers in Washington, D.C., seem to have an affinity for the cruder LLS. (Think the “sh-word.”)
Eisenstein said some of this usage clearly is shaped by the 140-character limit of Twitter messages, but geography's influence also is apparent.
Tracking tweeters
The statistical model the CMU team used to recognize regional
variation in word use and topics could predict the location of a
microblogger in the continental United States with a median error
of about 300 miles.
Studies of regional dialects traditionally have been based
primarily on oral interviews, Eisenstein said, noting that written
communication often is less reflective of regional influences
because writing -- even in blogs -- tends to be formal and thus
homogenized.
But Twitter offers a new way of studying regional lexicon, he
explained, because tweets are informal and conversational.
Furthermore, people who tweet using mobile phones have the option
of geotagging their messages with GPS coordinates.
For this study, Eisenstein and his co-authors -- Eric P. Xing,
associate professor of machine learning, Noah A. Smith, assistant
professor in the Language Technologies Institute (LTI), and Brendan
O'Connor, machine learning graduate student -- collected a week's
worth of Twitter messages in March 2010, and selected geotagged
messages from Twitter users who wrote at least 20 messages. That
yielded a data base of 9,500 users and 380,000 messages.
Tracking vs. profiling
Though the researchers could pinpoint the users' locations using
the geotags, they can only guess as to their profiles.
Eisenstein said it's reasonable to assume that people sending lots
of tweets from mobile phones are younger than the average Twitter
user and the topics discussed by these users seem to reflect
that.
Automated analysis of Twitter message streams offers linguists an
opportunity to watch regional dialects evolve in real time.
"It will be interesting to see what happens. Will 'suttin' remain a
word we see primarily in New York City, or will it spread?"
Eisenstein asked.
Eisenstein said it might be a mistake to assume the greater
interconnectivity afforded by the Internet will necessarily result
in more homogeneity in language since social circles maintained by
sites like Twitter are often geographically focused.
Also, many people use the Internet to seek out like-minded people
with similar interests, rather than expose themselves to a broader
range of ideas and experiences.
Eisenstein will present the study on Jan. 8 at the Linguistic Society of
America annual meeting in Pittsburgh. The paper is currently
available online.
