Tweeting with an accent

I’m writing this blog post from a cute little tea shop in Victoria, BC. I’m up here to present at the Northwest Linguistics Conference, which is a yearly conference for both Canadian and American linguists (yes, I know Canadians are Americans too, but United Statsian sounds weird), and I thought that my research project may be interesting to non-linguists as well. Basically, I investigated whether it’s possible for Twitter users to “type with an accent”. Can linguists use variant spellings in Twitter data to look at the same sort of sound patterns we see in different speech communities?

Picture of a bird saying

Picture of a bird saying “Let’s Tawk”. Taken from the website of the Center for the Psychology of Women in Seattle. Click for link.

So if you’ve been following the Great Ideas in Linguistics series, you’ll remember that I wrote about sociolinguistic variables a while ago. If you didn’t, sociolinguistic variables are sounds, words or grammatical structures that are used by specific social groups. So, for example, in Southern American English (representing!) the sound in “I” is produced with only one sound, so it’s more like “ah”.

Now, in speech these sociolinguistic variables are very well studied. In fact, the Dictionary of American Regional English was just finished in 2013 after over fifty years of work. But in computer mediated communication–which is the fancy term for internet language–they haven’t been really well studied. In fact, some scholars suggested that it might not be possible to study speech sounds using written data. And on the surface of it, that does make sense. Why would you expect to be able to get information about speech sounds from a written medium? I mean, look at my attempt to explain an accent feature in the last paragraph. It would be far easier to get my point across using a sound file. That said, I’d noticed in my own internet usage that people were using variant spellings, like “tawk” for “talk”, and I had a hunch that they were using variant spellings in the same way they use different dialect sounds in speech.

While hunches have their place in science, they do need to be verified empirically before they can be taken seriously. And so before I submitted my abstract, let alone gave my talk, I needed to see if I was right. Were Twitter users using variant spellings in the same way that speakers use different sound patterns? And if they are, does that mean that we can investigate sound  patterns using Twitter data?

Since I’m going to present my findings at a conference and am writing this blog post, you can probably deduce that I was right, and that this is indeed the case. How did I show this? Well, first I picked a really well-studied sociolinguistic variable called the low back merger. If you don’t have the merger (most African American speakers and speakers in the South don’t) then you’ll hear a strong difference between the words “cot” and “caught” or “god” and “gaud”. Or, to use the example above, you might have a difference between the words “talk” and “tock”. “Talk” is little more backed and rounded, so it sounds a little more like “tawk”, which is why it’s sometimes spelled that way. I used the Twitter public API and found a bunch of tweets that used the “aw” spelling of common words and then looked to see if there were other variant spellings in those tweets. And there were. Furthermore, the other variant spellings used in tweets also showed features of Southern American English or African American English. Just to make sure, I then looked to see if people were doing the same thing with variant spellings of sociolinguistic variables associated with Scottish English, and they were. (If you’re interested in the nitty-gritty details, my slides are here.)

Ok, so people will sometimes spell things differently on Twitter based on their spoken language dialect. What’s the big deal? Well, for linguists this is pretty exciting. There’s a lot of language data available on Twitter and my research suggests that we can use it to look at variation in sound patterns. If you’re a researcher looking at sound patterns, that’s pretty sweet: you can stay home in your jammies and use Twitter data to verify findings from your field work. But what if you’re not a language researcher? Well, if we can identify someone’s dialect features from their Tweets then we can also use those features to make a pretty good guess about their demographic information, which isn’t always available (another problem for sociolinguists working with internet data). And if, say, you’re trying to sell someone hunting rifles, then it’s pretty helpful to know that they live in a place where they aren’t illegal. It’s early days yet, and I’m nowhere near that stage, but it’s pretty exciting to think that it could happen at some point down the line.

So the big take away is that, yes, people can tweet with an accent, and yes, linguists can use Twitter data to investigate speech sounds. Not all of them–a lot of people aren’t aware of many of their dialect features and thus won’t spell them any differently–but it’s certainly an interesting area for further research.

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“Men” vs. “Females” and sexist writing

So, I have a confession to make. I actually set out to write a completely different blog post. In searching Wikimedia Commons for a picture, though, I came across something that struck me as odd. I was looking for pictures of people writing, and I noticed that there were two gendered sub-categories, one for men and one for women. Leaving aside the question of having only two genders, what really stuck out to me were the names. The category with pictures of men was called “Men Writing” and the category with pictures of women was called “Females Writing”.

Family 3

According to this sign, the third most common gender is “child”.

So why did that bother me? It is true that male humans are men and that women are female humans. Sure, a writing professor might nag about how the two terms lack parallelism, but does it really matter?

The thing is, it wouldn’t matter if this was just a one-off thing. But it’s not. Let’s look at the Category: Males and Category: Females*. At the top of the category page for men, it states “This category is about males in general. For human males, see Category:Male humans”. And the male humans category is, conveniently, the first subcategory. Which is fine, no problem there. BUT. There is no equivalent disclaimer at the top of Category: Females, and the first subcategory is not female humans but female animals. So even though “Females” is used to refer specifically to female humans when talking about writing, when talking about females in general it looks as if at least one editor has decided that it’s more relevant for referring to female animals. And that also gels with my own intuitions. I’m more like to ask “How many females?” when looking at a bunch of baby chickens than I am when looking at a bunch of baby humans. Assuming the editors responsible for these distinctions are also native English speakers, their intuitions are probably very similar.

So what? Well, it makes me uncomfortable to be referred to with a term that is primarily used for non-human animals while men are referred to with a term that I associate with humans. (Or, perhaps, women are being referred to as “female men”, but that’s equally odd and exclusionary.)

It took me a while to come to that conclusion. I felt that there was something off about the terminology, but I had to turn and talk it over with my officemate for a couple minutes before finally getting at the kernel of the problem. And I don’t think it’s a concious choice on the part of the editors–it’s probably something they don’t even realize they’re doing. But I definitely do think that it’s related to the gender imbalance of the editors of Wikimedia. According to recent statistics, over ninety percent (!) of Wikipedia editors are male. And this type of sexist language use probably perpetuates that imbalance. If I feel, even if it’s for reasons that I have a hard time articulating, that I’m not welcome in a community then I’m less likely to join it. And that’s not just me. Students who are presented with job descriptions in language that doesn’t match thier gender are less likely to be interested in those jobs. Women are less likely to respond to job postings if “he” is used to refer to both men and women. I could go on citing other studies, but we could end up being here all day.

My point is this: sexist language affects the behaviour and choices of those who hear it. And in this case, it makes me less likely to participate in this on-line community because I don’t feel as if I would be welcomed and respected there. It’s not only Wikipedia/Wikimedia, either. This particular usage pattern is also something I associate with Reddit (a good discussion here). The gender breakdown of Reddit? About 70% male.

For some reason, the idea that we should avoid sexist language usage seems to really bother people. I was once a TA for a large lecture class where, in the middle of discussions of the effects of sexist language, a male student interrupted the professor to say that he didn’t think it was a problem. I’ve since thought about it quite a bit (it was pretty jarring) and I’ve come to the conclusion that the reason the student felt that way is that, for him, it really wasn’t a problem. Since sexist language is almost always exclusionary to women, and he was not a woman, he had not felt that moment of discomfort before.

Further, I think he may have felt that, because this type of language tends to benefit men, he felt that we were blaming him. I want to be clear here: I’m not blaming anyone for thier unconscious biases. And I’m  not saying that only men use sexist language. The Wikimedia editors who made this choice may very well have been women. What I am saying is that we need to be aware of these biases and strive to correct them. It’s hard, and it takes constant vigilance, but it’s an important and relatively simple step that we can all take in order to help eliminate sexism.

*As they were on Wednesday, April 8 2015. If they’ve been changed, I’d recommend the Way Back Machine.