Because Internet

by Gretchen McCulloch

  But these networks aren’t formed in isolation: people tend to follow others with similar interests and demographics. One study demonstrating this looked at the geographic spread of a couple thousand words that became massively more popular on Twitter between 2009 and 2012. It found that terms tended to leapfrog from one city to another based on demographic similarity, not just geographic proximity. So slang would spread between Washington, D.C., and New Orleans (both have high proportions of black people), Los Angeles and Miami (high proportions of Hispanic people), or Boston and Seattle (high proportions of white people), but not necessarily the cities in between. For example, the abbreviation “af” for “as fuck” (as in “word maps are cool af”) starts out at low levels in Los Angeles and Miami in 2009, then spreads elsewhere in California, the South, and around Chicago in 2011–2012, suggesting that it was spreading from Hispanic to African American populations. The study stops there, but we can continue: in 2014 and 2015, “af” started appearing in BuzzFeed headlines, a decent measure of when it came to be co-opted by mainstream brands capitalizing on its association with African American coolness.

  We’re especially likely to pick up new words when we’re first entering a community. Linguist Dan Jurafsky and his colleagues looked at over four million posts from members of RateBeer and BeerAdvocate, two online beer communities that have been around for more than a decade. They wanted to know how people’s language use changed the longer they’d been members of the forum. They found that older accounts were likely to stick to older pieces of beer jargon, such as talking about a beer’s “aroma” if they joined in 2003, whereas younger accounts were quicker to adopt newer beer jargon, such as preferring “S” (for “smell”) if they joined in 2005. The study provides an interesting way of teasing apart the effects of age and peer groups, suggesting that people are more open to new vocabulary during the first third of their lifespan, regardless of whether that’s an eighty-year lifespan in an offline community or a three-year “lifespan” in an online one.

  What’s unique about adolescence, then, may not be our susceptibility to linguistic trends. Rather, it’s the last time that a whole population is entering a new social group all at once. Adults periodically move to new cities and start new jobs and develop new hobbies, all of which bring us under new linguistic influences. But we don’t all change careers or become parents or join beer-tasting messageboards at exactly the same age, so it’s harder to study linguistic changes that happen later in life. Harder, but not impossible: it also depends on where we want to look. Researchers are part of society, and as a society, we’re more likely to be worried about teen slang than about parents adding new terms to the familect or businesspeople adopting new corporate buzzwords. Perhaps we need to rethink our demographic questions to ask about dates of joining new social groups in addition to date of birth.

  Finding networked language patterns on social media isn’t an anomaly: people offline are generally also more similar to their friends than to the rigid, unfeeling demographic boxes of a census-taker. It’s just that we had no practical way of measuring it. Doing a network analysis of people’s friends and interlocutors used to be really hard. Like makes-biking-around-France-for-four-years-look-easy kind of hard. You could start by doing a typical language survey, but that would just be the beginning of your work. You’d also have to get people to manually make a list of all their friends, how long they’ve known them, and how often they talk with each one. Then, you’d have to somehow get ahold of all these friends and also survey them. But that’s just a one-layer network. You’d want to repeat these steps several times so that you could make webs of connections between people. Social scientists have done this kind of research occasionally—there’s a city in Massachusetts called Framingham where researchers have followed a couple thousand people, with their health and social connections, for three generations now—but understandably, they don’t do it very often. Not for daily words produced by tens or hundreds of thousands of people. Even though your Twitter network doesn’t represent absolutely everyone you talk to, even though not everyone is on Twitter, it makes for an intriguing new way of approaching the very old question of how new words catch on.

  Analyzing language based on social networks also complicates another traditional demographic check box: gender. The traditional finding for gender is shown in a study by the linguists Terttu Nevalainen and Helena Raumolin-Brunberg at the University of Helsinki, which looked at six thousand personal letters written in English between 1417 and 1681. Personal letters make a great corpus because, like tweets, they don’t go through editorial standardization. Unfortunately, there’s also a lot fewer of them, and they tend to overrepresent the leisured, educated classes. But they’re still the best record we have of what day-to-day English looked like back then. The linguists examined fourteen language changes that occurred during this period, things like the eradication of “ye,” the switch from “mine eyes” to “my eyes,” and the replacement of -th with -s, making words like “hath,” “doth,” and “maketh” into “has,” “does,” and “makes.” (Pretty shocking stuff.) For eleven out of the fourteen changes, Nevalainen and Raumolin-Brunberg found that female letter-writers were changing the way they wrote faster than male letter-writers. In the three exceptional cases where the men were ahead of the women, those particular changes were linked to men’s greater access to education at the time. In other words, women are reliably ahead of the game when it comes to word-of-mouth linguistic changes.

  Research in other centuries, languages, and regions continues to find that women lead linguistic change, in dozens of specific changes in specific cities and regions. Young women are also consistently on the bleeding edge of those linguistic changes that periodically sweep through media trend sections, from uptalk (the distinctive rising intonation at the end of sentences?) to the use of “like” to introduce a quotation (“And then I was like, ‘Innovation’”). The role that young women play as language disruptors is so clearly established at this point it’s practically boring to linguists who study this topic: well-known sociolinguist William Labov estimated that women lead 90 percent of linguistic change in a paper he wrote in 1990. (I’ve attended more than a few talks at sociolinguistics conferences about a particular change in vowels or vocabulary, and it barely gets even a full sentence of explanation: “And here, as expected, we can see that the women are more advanced on this change than the men. Next slide.”) Men tend to follow a generation later: in other words, women tend to learn language from their peers; men learn it from their mothers.

  What’s less clear is why. Lots of reasons have been proposed, from the fact that women still dominate the caregiving of children in the societies studied, that women may pay more attention to language to compensate for relative lack of economic power or to facilitate social mobility, and that women tend to have more social ties. But in many cases, gender (like age) seems to be a proxy for other factors related to how we socialize with each other.

  Several internet studies have highlighted the importance of differentiating between gender and social context. One study, by linguists Susan Herring and John Paolillo, looked at how people write blogs. At first, it seemed like there was a significant gender difference in the language of blogs. But when they looked again, the linguists found that what was really going on was a genre difference: men were more likely to write topic-based blogs and women more likely to write diary-style blogs. But of course, there were also many people who didn’t pick the genre most typical for their gender. When the researchers compared within each genre, the original “gender” difference disappeared.

  Another study, looking at a corpus of 14,000 Twitter users, and guessing their gender based on the skew of their first name in census data, appeared at first glance to show clear gender differences: people with predominantly female names were more likely to use emoticons, for example, while people with male-associated names were more likely to swear. But when the researchers looked one
step further, they found that the words people most often tweeted formed natural clusters into over a dozen interest groups, such as sports fans, hip-hop fans, parents, politics buffs, TV and movie fans, techies, book fans, and so on. True, many of the groups had a gender skew, but none of them were absolute, and they also had clear associations with other demographic factors like age and race. Sometimes whole groups defied gender norms—men overall tended to swear more, but techies, a cluster that was male-dominated, didn’t swear much at all, presumably because they were using Twitter as an extension of the workplace. At the individual level, people followed the norms of their clusters rather than their genders—a woman in the sports cluster or a man in the parenting cluster tweeted like their fellow sports fans or parents, rather than like an “average woman” or “average man.” Moreover, restricting the analysis to accounts with names that showed a clear gender skew in census data excludes precisely those users that would complicate a binary view of gender, including nonbinary people and others who’ve deliberately chosen a non-census-gendered username.

  Offline, ethnographic research has also pointed to the importance of network factors. Linguist Lesley Milroy was doing a pretty standard study of language change in a couple working-class neighborhoods of Belfast, Northern Ireland. As with many communities, the young women were leading a linguistic change—in this case, changing the vowel in “car” to sound more like “care.” This vowel is common elsewhere in Northern Ireland, but it was new to this particular community, and it was the young women who were bringing it in. What was mystifying was how they were getting it. When Milroy asked the women who they were close to, they named friends, family, and coworkers, all from their neighborhood—the same neighborhood where no one else yet had this vowel change.

  In a later paper with James Milroy, the two figured out why by linking linguistic change to another concept in social science: strong and weak ties. Strong ties are people you spend a lot of time with and feel close to, who you share mutual friends with; weak ties are acquaintances who you may or may not share mutual ties with. In the case of the Belfast study, the early-adopting young women all worked at the same store in the city center, where people were already using the new vowel. Although they didn’t have close friends from the city center, they did have weak-tie contact with customers, which would have often exposed them to the new vowel—more than the young men of their neighborhood, who weren’t employed outside it.

  Milroy and Milroy figured that, just as your weak ties are a greater source of new information like gossip and employment opportunities than your close friends who already know the same things you do, more weak ties also lead to more linguistic change. To demonstrate, they compared the history of English and Icelandic. English and Icelandic have a common Germanic ancestor, and a millennium ago Old English and Old Norse (the ancestor of Old Icelandic spoken at the time) were still more or less mutually intelligible. But from there, their histories diverge. Icelandic has changed only a little: twenty-first-century Icelandic speakers can still read their Sagas from the thirteenth century, written in Old Icelandic, without much difficulty. English has changed a lot: although we can manage Shakespeare, from only four centuries ago, with the help of footnotes, even The Canterbury Tales (six centuries ago) requires a full translation or a course in Middle English to understand. This means that, despite the fact that it’s technically written in Old English rather than Old Icelandic, Icelanders would have an easier time learning to read Beowulf than would modern English speakers.

  Clearly, English has changed faster than Icelandic has over the same timespan. Milroy and Milroy proposed that the reason is weak ties. The thing to know about Iceland is that it’s got really close-knit communities. Icelandic surnames are still based on the given name of your father (or sometimes mother), which makes a lot more sense in a society where most of the people you meet already know your family, and this tendency to introduce oneself by naming an extensive network of relatives dates all the way back to the Sagas. If everyone you know already knows each other, your only source of new linguistic forms is random variation—you don’t have any weak ties to borrow from.

  English, on the other hand, has had several significant sources of weak ties over its history—invasions by the Danes and the Normans, a tradition of uprooting and moving to London and later other cities to seek one’s fortune, and imperial expansion of its own. True, the English-speaking world has its own small, tight-knit communities where everyone knows everyone else’s relatives (I still introduce myself by referring to my parents or grandparents at family reunions), but it also has many more big cities where you can be anonymous in a crowd or have three different friend groups who never meet each other. What’s more, the map studies from the beginning of this chapter tell us that within English, it’s the bigger, looser-knit cities that give rise to more linguistic change.

  But weak ties can’t be the only factor. After all, it’s also clear that we talk like people in our social circles, whether that’s French villages, Detroit jocks, or familects—all examples of strong ties. How can both strong and weak ties be responsible for how we speak? And how can we map out exactly who says what to who over a large population for a couple centuries, long enough for several changes to run their course? That’s not just bicycling—that’s time travel.

  Linguist Zsuzsanna Fagyal and colleagues solved both problems using a computer simulation. They made a network of nine hundred hypothetical people over forty thousand turns. Each person had a certain number of ties to other people in the network and started with a randomly assigned value for a hypothetical linguistic feature, like how you might call the thing you drink water from in a school a “water fountain” but your neighbor might call it a “drinking fountain.” Then, at each turn, each person looked to the other people they were connected to and had a certain probability of adopting their version of the feature, like how you might start saying “drinking fountain” if you have a friend who uses the term. If you do pick it up, that word now becomes yours as well, and the people you’re connected to might pick it up from you the next round. They repeated this turn process forty thousand times, with three different kinds of networks. In one version, the entire network was made up of close ties: everyone was well connected to the rest of the network. This dense network behaved like Iceland: one linguistic option caught on very quickly and stayed completely dominant for the rest of the simulation. In another version, the entire network was made up of weak ties and no one was well connected. The loose network behaved like a world of tourists: all of the options stuck around and none of them ever became dominant. But in the most interesting simulation, they made some of the nodes highly connected “leaders” and others less connected “loners.” This mixed network behaved like English: one option would catch on for a while, but the other options would never totally disappear, and eventually one of them would become popular instead—a cycle that repeated several times. The researchers concluded that both strong and weak ties have an important role to play in linguistic change: the weak ties introduce the new forms in the first place, while the strong ties spread them once they’re introduced.

  The internet, then, makes language change faster because it leads to more weak ties: you can remain aware of people who you don’t see anymore, and you can get to know people who you never would have met otherwise. The phenomenon of a hashtag or funny video going viral is an example of the power of weak ties—when the same thing is shared only through strong ties, it ends up merely as an inside joke. But the internet doesn’t lead to the collapse of strong ties, either: the average person has a small handful of people who they message on a regular basis, between four and twenty-six, depending on how you count. What’s more, social networking sites that prompt you to interact with denser ties—people you already know and friends of friends—tend to be less linguistically innovative. It’s not an accident that Twitter, where you’re encouraged to follow people you don’t already know, has given rise to mo
re linguistic innovation (not to mention memes and social movements) than Facebook, where you primarily friend people you already know offline.

  But geography and demographics and even networks aren’t destiny. In addition to having some amount of choice in where we live and who we associate with, we also have a certain amount of control over how much we want to be influenced by our interlocutors: who we want to project ourselves to be, linguistically speaking.

  Attitudes

  If you want to sum up Canada in a headline, you might reach for the catchphrase “from Eh to Zed.” You’d be in good company: this slogan features in the titles of three books, items like t-shirts and YouTube videos, and news articles about everything from sports to the language itself. But what many people don’t think about, even Canadians, is that small Canadian children often call the last letter of the alphabet “zee” instead. Normally, when linguists see a word or construction that’s common among parents but not their kids, we simply conclude that there’s a change going on—that in another generation it’ll be a grandparent-y sort of word, and eventually pass into history. “Chesterfield” is doing exactly this in Canada: it’s been receding for decades in favor of “couch.”

  But “zed” has been acting really weird. The linguist J. K. Chambers did a survey of Canadian twelve-year-olds in the 1970s, and found that two-thirds of them said “zee”—but when he went back and surveyed the same population in the 1990s, he found that the vast majority were now using “zed” as adults. The same shift happened with successive generations. Chambers figured that children learn “zee” from the alphabet song and American children’s television programs like Sesame Street, but when they get older, they learn that “zed” is associated with Canadian identity and switch. Indeed, noted Chambers, “zed” is one of the first things that American immigrants to Canada change about their speech, “because calling it ‘zee’ unfailingly draws comments from the people they are talking to.”