...but we also learned how to map data using newer, more sophisticated computational methods. For instance, reading geographic data from a comma separated file and mapping the data in the R programming language. More importantly, we learned what the interaction between geographic features, historical migrations, and a 'snapshot' of linguistic data can tell us about our language and ourselves.
Now, in the late 1800s, there were basically two ways that you could collect data for linguistic atlases: informally known as the German Method, and the French Method. The German Method was the method Georg Wenker used in 1876, when he sent out 50,000 surveys to German schoolmasters who dutifully sent back 45,000 completed surveys. The flaw in this method is that there is no guarantee of standardization as far as how the data is collected and interpreted. The French Method is what Jules Gilliéron used a decade later: send one trained linguist galavanting around the countryside on a bicycle for four years, eating baguettes, drinking wine, and conducting sociolinguistic interviews with everyone he can as he moves from town to town. My kind of job! Both methods resulted in gorgeous, detailed, and informative atlases...decades after the data were collected. More recently, enterprising linguists (among them, Dr. Labov) conducted telephone surveys, resulting in the "gold standard" Atlas of North American English. The ANAE gives an enormous amount of granularity to the study of regional dialects in North America -- seriously, click the link and play around, it's awesome.
Big Data
What the ANAE achieves, it does with a mere 792 speakers, intelligently sampled by region. It is a feat of ingenuity and economy.
However, we now have some intriguing new tools at our disposal, thanks to the internet and social media platforms like Facebook and Twitter. To give you an idea: a search for the word "the," -- a pretty good proxy for English use -- returns 607 million tokens in the last month alone. All of it is literally published work. It is, in effect, an enormous corpus of written language. Given the right tools and know-how, anyone can search that published material.
The Speech Problem: Graffiti and The Writing on the (Facebook) Wall
The only hitch is this: writing is not speech. In fact, if you try to figure out how English speakers anywhere pronounce English based on the spelling conventions of academic written English, you're gonna have a bad time. A few sound shifts here, a few hundred years of weird convention there, and you've got a system that doesn't tell you much of anything useful.
Notice, though, I said the spelling conventions of academic English. Many people have a pet peeve they're more than willing to share (especially on reddit, it seems): they hate when others write should of in lieu of should have. This kind of mistake is any historical linguist's favorite thing ever. Why? Because it tells us something about pronunciation. People who write should of have reduced should have to should've and it is coming out in their writing -- should of and should've are totally indistinguishable in casual speech.
It's precisely this kind of error, along with the writings of hand-wringing pedants lamenting the decline of language (among other things), that allow us to reconstruct the pronunciation of Latin as it changed through time. (aside: ever wonder why it's "inconvenient" but not "inpolite"? A historical linguist can tell you why, and when it happened). In fact, we get an enormous amount of phonologically relevant information from things like graffiti dick jokes in places like Pompeii Who says historical linguistics isn't fun?
Error isn't the whole picture though. It's one thing to say that people who struggle spelling will fall back on sounding things out. It's quite another when the non-standard spelling is intentional. For instance, one task for computational linguists interested in Natural Language Processing (NLP) is to group various spellings into sets that computers can recognize are all the same word. To simplify: a computer needs to know that color and colour are the same thing if it's going to process language quickly and effectively. Recent research in NLP has demonstrated that people on social medial platforms intentionally write how they speak. That is, they go out of their way to spell things in a non-standard way in order to better communicate how they talk informally. The best part is that this research holds across languages. While an American might be sittin (instead of sitting), a Dutch user of Twitter may well sitte (instead of zitten). This is especially true the further a dialect diverges from the written standard, as in modern dialects of Arabic. It's also true in AAVE, where the orthography you learn in school can't capture the phonological and grammatical nuances of the dialect -- something that writers like Zora Neal Hurston, Toni Morrison, and Ralph Ellison grappled with.
Black Twitter: Stigmatized Speech, Innovative Writing
Around the time I was taking the class on dialect geography, I stumbled upon a Youtube video purporting to explain #Blackfolkslang. It's a fun example of what linguists call enregisterment: when a dialect feature gets (consciously) noticed and becomes an overt marker of linguistic belonging. A classic example is the stereotypical Brooklynese fugeddaboudit.
Being a native speaker of AAVE (due to childhood speech community), the forms made intuitive sense to me and were a lot of fun. When I showed them to non-speakers of the dialect out of context, however, they were baffled. "What is ioneem? Is that Arabic?"
I thought it would be fun to dig into their use, and see where these forms were used, and how often. I got help writing a script in Python, using the Twitter API and the Twython package to extract tweets, and started using the mapping tools I was learning in R to check them out.
It became an obsession.
A few months and a few hundred thousands tweets later, I came to a few realizations. First, there's not consensus. Some people tweet nun (for "nothing"), while others tweet nuttin, and others still tweet nuffin. Second, the forms used vary regionally. Third, the phonological clues these tweets provide can be corroborated by both other media and linguistic informants (informant: a fancy term for people who both speak whatever a linguist is interested in and are willing to talk to one). Lastly, there's not just one "Black Twitter." The Black Twitter that blogs, contributes to NPR, and live-tweets sociology conferences was not the Black Twitter I was reading. I was reading tweets from young adults not represented in the Pew Research Center Internet Project, from young gang members who signal affiliation with spelling (fun fact: crips superstitiously avoid the combo "ck" because it could stand for "crip killa," and will instead favor spellings like "fucc"), and from people who use Twitter as a free analog to both texting plans and dating sites.
Some of the writing was not immediately recognizable. For instance, I was perplexed by yeen for "you ain't" (in part because it's not used in NYC or Philadelphia, I would later find). That is, I was perplexed right until I searched for it on YouTube, and came across dozens of different songs, often self-produced, which use yeen in the lyrics. Similarly, nun could conceivably be pronounced in a number of different ways. French Montana to the rescue! People often tweet lyrics to their favorite songs, and quite a number of them tweeted "nigga i ain't worried bout nun". Whether there is a glottal stop or it's elided for some of these tweeters is not clear, but what is clear is that it is two syllables, not one -- the only way to fit the rhythm.
Ultimately, I gathered data on ~30 terms (among them: yeen, talmbout, eem, ion, sumn -- you ain't, talking about, even, I don't, and something, respectively), and found that all of the variation could be explained by recourse to a handful of variations in pronunciation -- variations which can be corroborated by other means.
The Discovery: The Maps Don't Line Up
A handful of computationally minded linguists and linguistically minded computer scientists have been doing work on dialect geography using Twitter data, and I've found their work invaluable in developing this research. One of them, Gabriel Doyle (at UCSD), has demonstrated that dialect forms on Twitter correspond exceptionally well to the established gold standard of the ANAE. Like, uncannily, eerily well. He concluded, after some sophisticated statistical verification, that it's possible to glean geographic information about dialects from Twitter data.
His maps of double modals ("might could") and of the "needs washed" construction ("your car needs washed") line up perfectly with the maps produced by the ANAE and by the Harvard Dialect Survey (HDS).
My maps, however, did not line up.
Now, it has been known for a long time that including data from speakers of AAVE muddies things. In some ways, AAVE speakers do what other people in their general vicinity are doing, but in other ways they seem to do things differently. There's a large body of literature on this, but no national level description of regional variation in AAVE.
The standard maps of dialect regions in North America look like this: