Can your use of capitalization reveal your political affiliation?

This week, I’m in Vancouver this week for the meeting of the Association for Computational Linguistics. (On the subject of conferences, don’t forget that my offer to help linguistics students from underrepresented minorities with the cost of conferences still stands!) The work I’m presenting is on a new research direction I’m pursuing and I wanted to share it with y’all!

If you’ve read some of my other posts on sociolinguistics, you may remember that the one of its central ideas is that certain types of language usage pattern together with aspects of people’s social identities. In the US, for example, calling a group of people “yinz” is associated with being from Pittsburgh. Or in Spanish, replacing certain “s” sounds with “th” sounds is associated with being from northern or central Spain. When a particular linguistic form is associated with a specific part of someone’s social identity, we call that a “sociolinguistic variable”

There’s been a lot of work on the type of sociolinguistic variables people use when they’re speaking, but there’s been less work on what people do when they’re writing. And this does make a certain amount of sense: many sociolinguistic variables are either 1) something people aren’t aware they’re doing or 2) something that they’re aware they’re doing but might not consider “proper”. As a result, they tend not to show up in formal writing.

This is where the computational linguistics part comes in; people do a lot of informal writing on computers, especially on the internet. In fact, I’d wager that humans are producing more text now than at any other point in history, and a lot of it is produced in public places. That lets us look for sociolinguistics variables in writing in a way that wasn’t really possible before.

Which is a whole lot of background to be able to say: I’m looking at how punctuation and capitalization pattern with political affiliation on Twitter.

Political affiliation is something that other sociolinguists have definitely looked at. It’s also something that’s very, very noticeable on Twitter these days. This is actually a boon to this type of research. One of the hard things about doing research on Twitter is that you don’t always necessarily know someone’s social identity. And if you use a linguistic feature to try to figure out their identity when what you’re interested in is linguistic features, you quickly end up with the problem of circular logic.

Accounts which are politically active, however, will often explicitly state their political affiliation in their Twitter bio. And I used that information to get tweets from people I was very sure had a specific political affiliation.

For this project, I looked at people who use the hashtags #MAGA and #theResistance in their Twitter bios. The former is an initialism for “Make America Great Again” and is used by politically conservative folks who support President Trump. The latter is used by political liberal folks who are explicitly opposed to President Trump. These two groups not only have different political identities, but also are directly opposed to each other. This means there’s good reason to believe that they will use language in different ways that reflect that identity.

But what about the linguistic half of the equation? Punctuation and capitalization are especially interesting to me because they seem to be capturing some of the same information we might find in prosody or intonation in spoken language. Things like YELLING or…pausing….or… uncertainty?  They’re also much, much easier to measure punctuation than intonation, which is notoriously difficult and time-consuming to annotate.  At the same time, I have good evidence that how you use punctuation and capitalization has some social meaning. Check out this tweet, for example:

0b1022106daeb0d0419263dcf9c5aa93--this-is-me-posts

As this tweet shows, putting a capital letter at the beginning of a tweet is anything but “aloof and uninterested yet woke and humorous”.

So, if punctuation and capitalization are doing something socially, is part of what they’re doing expressing political affiliation?

That’s what I looked into. I grabbed up to 100 tweets each from accounts which used either #MAGA or #theResistance in their Twitter bios. Then I looked at how much punctuation and capitalization users from these two groups used in their tweets.

Punctuation

First, I looked at all punctuation marks. I did find that, on average, liberal users tended to use less punctuation. But when I took a closer look at the data, an interesting pattern emerged. In both the liberal and conservative groups, there were two clusters of users: those who used a lot of punctuation and those who used almost none.

punctuation

Politically liberal users on average tended to use less punctuation than politically conservative users, but in both groups there’s really two sets of users: those who use a lot of punctuation and those who use basically  none. There just happen to be more of the latter in #theResistance.

What gives rise to these two clusters? I honestly don’t know, but I do have a hypothesis. I think that there’s  probably a second social variable in this data that I wasn’t able to control for. It seems likely that the user’s age might have something to do with it, or their education level, or even whether they use thier Twitter account for professional or personal communication.

Capitalization

My intuition that there’s a second latent variable at work in this data is even stronger given the results for the amount of capitalization folks used. Conservative users tended to use more capitalization than the average liberal user, but there was a really strong bi-modal distribution for the liberal accounts.

Rplot

Again, we see that conservative accounts use more of the marker (in this case capitalization), but that there’s a strong bi-modal distribution in the liberal users’ data.

What’s more, the liberal accounts that used a lot of punctuation also tended to use a lot of capitalization. Since these features are both ones that I associate with very “proper” usage (things like always starting a tweet with a capital letter, and ending it with a period) this seems to suggest that some liberal accounts are very standardized in their use of language, while others reject at least some of those standards.

So what’s the answer the question I posed in the title? Can capitalization or punctuation reveal political affiliation? For now, I’m going to go with a solid “maybe”. Users who use very little capitalization and punctuation are more likely to be liberal… but so are users who use a lot of both. And, while I’m on the subject of caveats, keep in mind that I was only looking at very politically active accounts who discuss thier politics in their user bios.  These observations probably don’t apply to all Twitter accounts (and certainly not across different languages).

If you’re interested in reading more, you can check out the fancy-pants versions of this research here and here.  And I definitely intend to consider looking at this; I’ll keep y’all posted on my findings. For now, however, off to find me a Nanimo bar!

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Where 👏 do 👏 the 👏 claps 👏 go 👏 when 👏 you 👏 write 👏 like 👏 this 👏?

You may already be familiar with the phenomena I’m going to be talking about today: when someone punctuates some text with the clap emoji. It’s a pretty transparent gestural scoring and (for me) immediately brings to mind the way my mom would clap with every word when she was particularly exasperated with my sibling and I (it was usually along with speech like “let’s go, let’s go, let’s go” or “get up now”). It looks like so:

This innovation, which started on Black Twitter is really interesting to me because it ties in with my earlier work on emoji ordering. I want to know where emojis go, particularly in relation to other words. Especially since people have since extended this usage to other emoji, like the US Flag:

Logically, there are several different ways you can intersperse clap emojis with text:

  • Claps 👏  are 👏 used 👏 between 👏 every 👏 word.
  •  👏 Claps 👏 are 👏 used 👏 around 👏 every 👏 word. 👏
  •  👏 Claps 👏 are 👏 used 👏 before 👏 every 👏 word.
  • Claps 👏 are 👏 used 👏 after 👏 every 👏 word. 👏
  • Claps 👏 are used 👏 between phrases 👏 not words

I want to know which of these best describes what people actually do. I’m not aiming to write an internet style guide, but I am hoping to characterize this phenomena in a general way: this is how most people who do this do it, and if you want to use this style in a natural way, you should probably do it the same way.

Data

I used Fireant to grab 10,000 tweets from the Twitter streaming API which had the clap emoji in them at least once. (Twitter doesn’t let you search for a certain number of matches of the same string. If you search for “blob” and “blob blob” you’ll get the same set of results.)

Analysis

From that set of 10,000 tweets, I took only the tweets that had a clap emoji followed by a word followed by another clap emoji and threw out any repeats. That left me with 260 tweets. (This may seem pretty small compared to my starting dataset, but there were a lot of retweets in there, and I didn’t want to count anything twice.) Then I removed @usernames, since those show up in the beginning of any tweet that’s a reply to someone, and URL’s, which I don’t really think of as “words”. Finally, I looked at each word in a tweet and marked whether it was a clap or not. You can see the results of that here:

timecourse

The “word” axis represents which word in the tweet we’re looking at: the first, second, third, etc. The red portion of the bar are the words that are the clap emoji. The yellow portion is the words that aren’t. (BTW, big shoutout to Hadley Wickham’s emo(ji) package for letting me include emoji in plots!)

From this we can see a clear pattern: almost no one starts a tweet with an emoji, but most people follow the first word with an emoji. The up-down-up-down pattern means that people are alternating the clap emoji with one word. So if we look back at our hypotheses about how emoji are used, we can see right off the bat that three of them are wrong:

  • Claps 👏  are 👏 used 👏 between 👏 every 👏 word.
  •  👏 Claps 👏 are 👏 used 👏 around 👏 every 👏 word. 👏
  •  👏 Claps 👏 are 👏 used 👏 before 👏 every 👏 word.
  • Claps 👏 are 👏 used 👏 after 👏 every 👏 word. 👏
  • Claps 👏 are used 👏 between phrases 👏 not words

We can pick between the two remaining hypotheses by looking at whether people are ending thier tweets with a clap emoji. As it turns out, the answer is “yes”, more often than not.

endWithClap

If they’re using this clapping-between-words pattern (sometimes called the “ratchet clap“) people are statistically more likely to end their tweet with a clap emoji than with a different word or non-clap emoji. This means the most common pattern is to use 👏 a 👏 clap 👏 after 👏 every 👏 word, 👏  including  👏 the  👏 last. 👏

This makes intuitive sense to me. This pattern is mimicking someone is clapping on every word. Since we can’t put emoji on top of words to indicate that they’re happening at the same time, putting them after makes good intuitive sense. In some sense, each emoji is “attached” to the word that comes before it in a similar way to how “quickly” is “attached” to “run” in the phrase “run quickly”. It makes less sense to put emoji between words, becuase then you end up with less claps than words, which doesn’t line up well with the way this is done in speech.

The “clap after every word” pattern is also what this website that automatically puts claps in your tweets does, so I’m pretty positive this is a good characterization of community norms.

 

So there you have it! If you’re going to put clap emoji in your tweets, you should probably do 👏 it 👏 like 👏 this. 👏 It’s not wrong if you don’t, but it does look kind of weird.

How many people in the US don’t have an accent?

First, the linguist’s answer: none. Zero. Everyone who uses a language uses a variety of that language, one that reflects their social identity–including things like gender, socioeconomic status or regional background.

But the truth is that some people, especially in the US, have the social privileged of being considered “unaccented”.  I can’t count how many times I’ve been “congratulated” by new acquaintances on having “gotten rid of” my Virginia accent. The thing is, I do have a lot of linguistic features from Tidewater/Piedmont English, like a strong distinction between the vowels in “body” and “baudy”, “y’all” for the second person plural and calling a drive-through liquor store a “brew thru” (shirts with this guy on them were super popular in my high school). But, at the same time, I also don’t have a lot of strongly stigmatized features, like dropping r’s or strong monopthongization you’d hear from a speaker like Virgil Goode (although most folks don’t really sound like that anymore). Plus, I’m young, white, (currently) urban and really highly educated. That, plus the fact that most people don’t pick up on the Southern features I do have, means that I have the privilege of being perceived as accent-less.

You_all_and_Y'all
Map showing the distribution of speakers in the United States who use “y’all”.

But how many people in the US are in the same boat as I am? This is a difficult question, especially given that there is no wide consensus about what “standard”, or “unaccented”, American English is. There is, however, a lot of discussion about what it’s not. In particular, educated speakers from the Midwest and West are generally considered to be standard speakers by non-linguists. Non-linguists also generally don’t consider speakers of African American English and Chicano English to be “standard” speakers (even though both of these are robust, internally consistent language varieties with long histories used by native English speakers).  Fortunately for me, the United States census asks census-takers about their language background, race and ethnicity, educational attainment and geographic location, so I could use census data to roughly estimate how many speakers of “standard” English there are in the United States. I chose to use the 2011 census, as detailed data on language use has been released for that year on a state-by-state basis (you can see a summary here).

From this data, I calculated how many individuals were living in states assigned by the U.S. Census Bureau to either the West or Midwest and how many residents surveyed in these states reported speaking English ‘very well’ or better. Then, assuming that residents of these states had educational attainment rates representative of national averages, I estimated how many college educated (with a bachelor’s degree or above) non-Black and non-Hispanic speakers lived in these areas.

So just how many speakers fit into this “standard” mold? Fewer than you might expect! You can see the breakdown below:

Speakers in the 2011 census who…

Count

% of US Population

…live in the United States…

311.7 million

100%

…and live in the Midwest or West…

139,968,791

44.9%

…and speak English at least ‘very well’…

127,937,178

41%

…and are college educated…

38,381,153 (estimated)

12.31%

…and are not Black or Hispanic.

33,391,603 (estimated)

10.7%

Based on the criteria laid out above, only around a tenth of the US population would count as ‘standard’ speakers. Now, keep in mind this estimate is possibly somewhat conservative: not all Black speakers use African American English and not all Hispanic speakers use Chicano English, and the regional dialects of some parts of the Northeast are also sometimes considered “standard”, which isn’t reflected in my rough calculation. That said, I think there’s still something if a large majority of Americans don’t speak what we might consider “standard” English, maybe it’s time to start redefining who gets to be the standard.

Are “a female” and “a male” used differently?

In this first part of this two-post series, I looked at how “a male” and “a female” were used on Twitter. I found that one part of speech tagger tagged “male” as a proper noun really frequently (which is weird, cause it isn’t one) and that overall the phrase “a female” was waaaay more frequent. Which is  interesting in itself, since my initial question was “are these terms used differently?” and these findings suggest that they are. But the second question is how are these terms used differently? To answer this, we’ll need to get a little more qualitative with it.

Using the same set of tweets that I collected last time, I randomly selected 100 tweets each from the “a male” and “a female” dataset. Then I hand tagged each subset of tweets for two things: the topic of the tweet (who or what was being referred to as “male” or “female”) and the part of speech of “male”  or “female”.

Who or what is being called “male” or “female”?

Rplot

Because there were so few tweets to analyze, I could do a content analysis. This is a methodology that is really useful when you don’t know for sure ahead of time what types of categories you’re going to see in your data. It’s like clustering that a human does.

Going into this analysis, I thought that there might be a difference between these datasets in terms of how often each term was used to refer to an animal, so I tagged tweets for that. But as I went through the tweets, I was floored by the really high number of tweets talking about trans people, especially Mack Beggs, a trans man from Texas who was forced to wrestle in the women’s division. Trans men were referred to as “a male” really, really often. While there wasn’t a reliable difference between how often “a female” and “a male” was used to refer to animals or humans, there was a huge difference in terms of how often they were  used to refer to trans people. “A male” was significantly more likely to be used to describe a trans person than “a female” (X2 (2, N = 200) = 55.33, p <.001.)

Part of Speech

Since the part of speech taggers I used for the first half of my analysis gave me really mixed results, I also hand tagged the part of speech of “male” or “female” in my samples. In line with my predictions during data collection, the only parts of speech I saw were nouns and adjectives.

When I looked at just the difference between nouns and adjectives, there was a little difference, but nothing dramatic. Then, I decided to break it down a little further. Rather than just looking at the differences in part of speech between “male” and “female”, I looked at the differences in part of speech and whether the tweet was about a trans person or a cis (not trans) person.

Rplot01

For tweets with “female”, it was used as a noun and an adjective at pretty much the same rates regardless of whether someone was talking about a trans person or a cis (non-trans) person. For tweets with “male”, though, when the tweet was about a trans person, it was used almost exclusively as a noun.

And there was a huge difference there. A large majority of tweets with “a male” and talking about a trans person used “male” as a noun. In fact, more than a third of my subsample of tweets using “a male” were using it as a noun to talk about someone who was trans.

So what’s going on here? This construction (using “male” or “female” as a noun to refer to a human) is used more often to talk about:

  1. Women. (Remember that in the first blog post looking at this, I found that “a female” is twice a common as “a male.)
  2. Trans men.

These both make sense if you consider the cultural tendency to think about cis men as, in some sense, the “default”. (Deborah Tannen has a really good discussion of this her article “Marked Women, Unmarked Men“. “Marked” is a linguistics term which gets used in a lot of ways, but generally means something like “not the default” or “the weird one”.) So people seem to be more likely to talk about a person being “a male” or “a female” when they’re talking about anyone but a cis man.

A note on African American English

giphy.gif

I should note that many of the tweets in my sample were African American English, which is not surprising given the large Black community on Twitter, and that use of “female” as a noun is a feature of this variety.  However, the parallel term used to refer to men in this variety is not “a man” or even “a male”, but rather “nigga”, with that spelling. This is similar to “dude” or “guy”: a nonspecific term for any man, regardless of race, as discussed at length by Rachel Jeantal here. You can see an example of this usage in speech above (as seen in the Netflix show “The Unbreakable Kimmy Schmidt“) or in this vine. (I will note, however, that it only has this connotation if used by a speaker of African American English. Borrowing it into another variety, especially if the speaker is white, will change the meaning.)

Now, I’m not a native user of African American English, so I don’t have strong intuitions about the connotation of this usage. Taylor Amari Little (who you may know from her TEDx talk on Revolutionary Self-Produced Justice) is, though, and tweeted this (quoted with permission):

If they call women “females” 24/7, leave em alone chile, run away

And this does square with my own intuitions: there’s something slightly sinister about someone who refers to women exclusively as “females”. As journalist Vonny Moyes pointed out in her recent coverage of ads offering women free rent in exchange for sexual favors, they almost refer to women as “girls or females – rarely ever women“. Personally, I find that very good motivation not to use “a male” or “a female” to talk about any human.

Can what you think you know about someone affect how you hear them?

I’ll get back to “a male/a female” question in my next blog post (promise!), but for now I want to discuss some of the findings from my dissertation research. I’ve talked about my dissertation research a couple times before, but since I’m going to be presenting some of it in Spain (you can read the full paper here), I thought it would be a good time to share some of my findings.

In my dissertation, I’m looking at how what you think you know about a speaker affects what you hear them say. In particular, I’m looking at American English speakers who have just learned to correctly identify the vowels of New Zealand English. Due to an on-going vowel shift, the New Zealand English vowels are really confusing for an American English speaker, especially the vowels in the words “head”, “head” and “had”.

tokensVowelPlot

This plot shows individual vowel tokens by the frequency of thier first and second formants (high-intensity frequency bands in the vowel). Note that the New Zealand “had” is very close to the US “head”, and the New Zealand “head” is really close to the US “hid”.

These overlaps can be pretty confusing when American English speakers are talking to New Zealand English speakers, as this Flight of the Conchords clip shows!

The good news is that, as language users, we’re really good at learning new varieties of languages we already know, so it only takes a couple minutes for an American English speaker to learn to correctly identify New Zealand English vowels. My question was this: once an American English speaker has learned to understand the vowels of New Zealand English, how do they know when to use this new understanding?

In order to test this, I taught twenty one American English speakers who hadn’t had much, if any, previous exposure to New Zealand English to correctly identify the vowels in the words “head”, “heed” and “had”. While I didn’t play them any examples of a New Zealand “hid”–the vowel in “hid” is said more quickly in addition to having different formants, so there’s more than one way it varies–I did let them say that they’d heard “hid”, which meant I could tell if they were making the kind of mistakes you’d expect given the overlap between a New Zealand “head” and American “hid”.

So far, so good: everyone quickly learned the New Zealand English vowels. To make sure that it wasn’t that they were learning to understand the one talker they’d been listening to, I tested half of my listeners on both American English and New Zealand English vowels spoken by a second, different talker. These folks I told where the talker they were listening to was from. And, sure enough, they transferred what they’d learned about New Zealand English to the new New Zealand speaker, while still correctly identifying vowels in American English.

The really interesting results here, though, are the ones that came from the second half the listeners. This group I lied to. I know, I know, it wasn’t the nicest thing to do, but it was in the name of science and I did have the approval of my institutional review board, (the group of people responsible for making sure we scientists aren’t doing anything unethical).

In an earlier experiment, I’d played only New Zealand English as this point, and when I told them the person they were listening to was from America, they’d completely changed the way they listened to those vowels: they labelled New Zealand English vowels as if they were from American English, even though they’d just learned the New Zealand English vowels. And that’s what I found this time, too. Listeners learned the New Zealand English vowels, but “undid” that learning if they thought the speaker was from the same dialect as them.

But what about when I played someone vowels from their own dialect, but told them the speaker was from somewhere else? In this situation, listeners ignored my lies. They didn’t apply the learning they’d just done. Instead, the correctly treated the vowels of thier own dialect as if they were, in fact, from thier dialect.

At first glance, this seems like something of a contradiction: I just said that listeners rely on social information about the person who’s talking, but at the same time they ignore that same social information.

So what’s going on?

I think there are two things underlying this difference. The first is the fact that vowels move. And the second is the fact that you’ve heard a heck of a lot more of your own dialect than one you’ve been listening to for fifteen minutes in a really weird training experiment.

So what do I mean when I say vowels move? Well, remember when I talked about formants above? These are areas of high acoustic energy that occur at certain frequency ranges within a vowel and they’re super important to human speech perception. But what doesn’t show up in the plot up there is that these aren’t just static across the course of the vowel–they move. You might have heard of “diphthongs” before: those are vowels where there’s a lot of formant movement over the course of the vowel.

And the way that vowels move is different between different dialects. You can see the differences in the way New Zealand and American English vowels move in the figure below. Sure, the formants are in different places—but even if you slid them around so that they overlapped, the shape of the movement would still be different.

formantDynamics

Comparison of how the New Zealand and American English vowels move. You can see that the shape of the movement for each vowel is really different between these two dialects.  

Ok, so the vowels are moving in different ways. But why are listeners doing different things between the two dialects?

Well, remember how I said earlier that you’ve heard a lot more of your own dialect than one you’ve been trained on for maybe five minutes? My hypothesis is that, for the vowels in your own dialect, you’re highly attuned to these movements. And when a scientist (me) comes along and tells you something that goes against your huge amount of experience with these shapes, even if you do believe them, you’re so used to automatically understanding these vowels that you can’t help but correctly identify them. BUT if you’ve only heard a little bit of a new dialect you don’t have a strong idea of what these vowels should sound like, so if you’re going to rely more on the other types of information available to you–like where you’re told the speaker is from–even if that information is incorrect.

So, to answer the question I posed in the title, can what you think you know about someone affect how you hear them? Yes… but only if you’re a little uncertain about what you heard in the first place, perhaps becuase it’s a dialect you’re unfamiliar with.

What’s up with calling a woman “a female”? A look at the parts of speech of “male” and “female” on Twitter .

This is something I’ve written about before, but I’ve recently had several discussions with people who say they don’t find it odd to refer to a women as a female. Personally, I don’t like being called “a female” becuase its a term I to associate strongly with talking about animals. (Plus, it makes you sound like a Ferengi.)  I would also protest men being called males, for the same reason, but my intuition is that that doesn’t happen as often. I’m willing to admit that my intuition may be wrong in this case, though, so I’ve decided to take a more data-driven approach. I had two main questions:

  • Do “male” and “female” get used as nouns at different rates?
  • Does one of these terms get used more often?

Data collection

I used the Twitter public API to collect two thousand English tweets, one thousand each containing the exact string “a male” and “a female”. I looked for these strings to help get as many tweets as possible with “male” or “female” used as a noun. “A” is what linguist call a determiner, and a determiner has to have a noun after it. It doesn’t have to be the very next word, though; you can get an adjective first, like so:

  • A female mathematician proved the theorm.
  • A female proved the theorm.

So this will let me directly compare these words in a situation where we should only be able to see a limited number of possible parts of speech & see if they differ from each other. Rather than tagging two thousand tweets by hand, I used a Twitter specific part-of-speech tagger to tag each set of tweets.

A part of speech tagger is a tool that guesses the part of speech of every word in a text. So if you tag a sentence like “Apples are tasty”, you should get back that “apples” is a plural noun, “are” is a verb and “tasty” is an adjective. You can try one out for yourself on-line here.

Parts of Speech

In line with my predictions, every instance of “male” or “female” was tagged as either a noun, an adjective or a hashtag. (I went through and looked at the hashtags and they were all porn bots. #gross #hazardsOfTwitterData)

However, not every noun was tagged as the same type of noun. I saw three types of tags in my data: NN (regular old noun), NNS (plural noun) and, unexpectedly, NNP (proper noun, singular). (If you’re confused by the weird upper case abbreviations, they’re the tags used in the Penn Treebank, and you can see the full list here.) In case it’s been a while since you studied parts of speech, proper nouns are things like personal or place names. The stuff that tend to get capitalized in English. The examples from the Penn Treebank documentation include “Motown”, “Venneboerger”,  and “Czestochwa”. I wouldn’t consider either “female” or “male” a name, so it’s super weird that they’re getting tagged as proper nouns. What’s even weirder? It’s pretty much only “male” that’s getting tagged as a proper noun, as you can see below:

maleVsFemalePOS

Number of times each word tagged as each part of speech by the GATE Twitter part-of-speech tagger. NNS is a plural noun, NNP a proper noun, NN a noun and JJ an adjective.

The differences in tagged POS between “male” and “female” was super robust(X2(6, N = 2033) = 1019.2, p <.01.). So what’s happening here?  My first thought was that it might be that, for some reason, “male” is getting capitalized more often and that was confusing the tagger. But when I looked into, there wasn’t a strong difference between the capitalization of “male” and “female”: both were capitalized about 3% of the time. 

My second thought was that it was a weirdness showing up becuase I used a tagger designed for Twitter data. Twitter is notoriously “messy” (in the sense that it can be hard for computers to deal with) so it wouldn’t be surprising if tagging “male” as a proper noun is the result of the tagger being trained on Twitter data. So, to check that, I re-tagged the same data using the Stanford POS tagger. And, sure enough, the weird thing where “male” is overwhelming tagged as a proper noun disappeared.

stanfordTaggerPOS

Number of times each word tagged as each part of speech by the Stanford POS tagger. NNS is a plural noun, NNP a proper noun, NN a noun, JJ an adjective and FW a “foreign word”.

So it looks like “male” being tagged as a proper noun is an artifact of the tagger being trained on Twitter data, and once we use a tagger trained on a different set of texts (in this case the Wall Street Journal) there wasn’t a strong difference in what POS “male” and “female” were tagged as.

Rate of Use

That said, there was a strong difference between “a female” and “a male”: how often they get used. In order to get one thousand tweets with the exact string “a female”, Twitter had to go back an hour and thirty-four minutes. In order to get a thousand tweets with “a male”, however, Twitter had to go back two hours and fifty eight minutes. Based on this sample, “a female” gets said almost twice as often as “a male”.

So what’s the deal?

  • Do “male” and “female” get used as nouns at different rates?  It depends on what tagger you use! In all seriousness, though, I’m not prepared to claim this based on the dataset I’ve collected.
  • Does one of these terms get used more often? Yes! Based on my sample, Twitter users use “a female” about twice as often as “a male”.

I think the greater rate of use of “a female” that points to the possibility of an interesting underlying difference in how “male” and “female” are used, one that calls for a closer qualitative analysis. Does one term get used to describe animals more often than the other? What sort of topics are people talking about when they say “a male” and “a female”? These questions, however, will have to wait for the next blog post!

In the meantime, I’m interested in getting more opinions on this. How do you feel about using “a male” and “a female” as nouns to talk about humans? Do they sound OK or strike you as odd?

My code and is available on my GitHub.

What does the National Endowment for the Humanities even do?

From the title, you might think this is a US-centric post. To a certain extent, it is. But I’m also going to be talking about topics that are more broadly of interest: what are some specific benefits of humanities research? And who should fund basic research? A lot has been written about these topics generally, so I’m going to be talking about linguistics and computational linguistics specifically.

This blog post came out of a really interesting conversation I had on Twitter the other day, sparked by this article on the potential complete elimination of both the National Endowment for the Humanities and the National Endowment for the Arts. During the course of the conversation, I realized that the person I was talking to (who was not a researcher, as far as I know) had some misconceptions about the role and reach of the NEH. So I thought it might be useful to talk about the role the NEH plays in my field, and has played in my own development as a researcher.

Curriculo

Oh this? Well, we don’t have funding to buy books anymore, so I put a picture of them in my office to remind myself they exist.

What does the NEH do?

I think the easiest way to answer this is to give you specific examples of projects that have been funded by the National Endowment for the Humanities, and talk about thier individual impacts. Keep in mind that this is just the tip of the iceberg; I’m only going to talk about projects that have benefitted my work in particular, and not even all of those.

  • Builds language teaching resources. One of my earliest research experiences was as a research assistance for Jack Martin, working with the Koasati tribe in Louisiana on a project funded by the NEH. The bulk of the work I did that summer was on a talking dictionary of the Koasati language, which the community especially wanted both as a record of the language and to support Koasati language courses. I worked with speakers to record the words for the dictionary, edit and transcribe the sound files to be put into the talking dictionaries. In addition to creating an important resource of the community, I learned important research skills that led me towards my current work on language variation. And the dictionary? It’s available on-line.
  • Helps fight linguistic discrimination. One of my main research topics is linguistic bias in automatic speech recognition (you can see some of that work here and here). But linguistic bias doesn’t only happen with computers. It’s a particularly pernicious form of discrimination that’s a big problem in education as well. As someone who’s both from the South and an educator, for example, I have purposefully cultivated my ability to speak mainstream American English becuase I know that, fair or not, I’ll be taken less seriously the more southern I sound. The NEH is at the forefront of efforts to help fight linguistic discrimination.
  • Document linguistic variation. This is a big one for my work, in particular: I draw on NEH-funded resources documenting linguistic variation in the United States in almost every research paper I write.

How does funding get allocated?

  • Which projects are funded is not decided by politicians. I didn’t realize this wasn’t common knowledge, but which projects get funded by federal funding agencies, including the NEH, NSF (which I’m currently being funded through) and NEA (National Endowment for the Arts) are not decided by politicians. This is a good thing–even the most accomplished politician can’t be expected to be an expert on everything from linguistics to history to architecture. You can see the breakdown of the process of allocating funding here.
  • Who looks at funding applications? Applications are peer reviewed, just like journal articles and other scholarly publications. The people looking at applications are top scholars in thier field. This means that they have a really good idea of which projects are going to have the biggest long-term impact, and that they can insure no one’s going to be reinventing the wheel.
  • How many projects are funded? All federal  research funding is extremely competitive, with many more applications submitted than accepted. At the NEH, this means as few as 6% of applications to a specific grant program will be accepted. This isn’t just free money–you have to make a very compelling case to a panel of fellow scholars that your project is truly exceptional.
  • What criteria are used to evaluate projects? This varies from grant to grant, but for the documenting endangered languages grant (which is what my work with the Koasati tribe was funded through), the evaluation criteria includes the following:
    • What is the potential for the proposed activity to
      1. Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
      2. Benefit society or advance desired societal outcomes (Broader Impacts)?
    • To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
    • Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
    • How well qualified is the individual, team, or organization to conduct the proposed activities?
    • Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Couldn’t this research be funded by businesses?

Sure, it could be. Nothing’s stopping companies from funding basic research in the humanities… but in my experience it’s not a priority, and they don’t. And that’s a real pity, because basic humanities research has a tendency of suddenly being vitally needed in other fields. Some examples from Natural Language Processing that have come up in just the last year:

  • Ethics: I’m currently taking what will  probably be my last class in graduate school. It’s a seminar course, filled with a mix of NLP researchers, electrical engineers and computer scientists, and we’re all reading… ethics texts. There’s been a growing awareness in the NLP and machine learning communities that algorithmic design and data selection is leading to serious negative social impacts (see this paper for some details). Ethics is suddenly taking center stage, and without the work of scholars working in the humanities, we’d be working up from first principles.
  • Pragmatics: Pragmatics, or the study of how situational factors affect meaning, is one of the more esoteric sub-disciplines in linguistics–many linguistics departments don’t even teach it as a core course. But one of the keynotes at the 2016 Empirical Methods in Natural Language Processing conference was about it (in NLP, conferences are the premier publication venue, so that’s a pretty big deal). Why? Because dialog systems, also known as chatbots, are a major research area right now. And modelling things like what you believe the person you’re talking to already knows is going to be critical to making interacting with them more natural.
  • Discourse analysis: Speaking of chatbots, discourse analysis–or the analysis of the structure of conversations–is another area of humanities research that’s been applied to a lot of computational systems. There are currently over 6000 ACL publications that draw on the discourse analysis literature. And given the strong interest in chatbots right now, I can only see that number going up.

These are all areas of research we’d traditionally consider humanities that have directly benefited the NLP community, and in turn many of the products and services we use day to day. But it’s hard to imagine companies supporting the work of someone working in the humanities whose work might one day benefit their products. These research programs that may not have an immediate impact but end up being incredibly important down-the-line is exactly the type of long-term investment in knowledge that the NEH supports, and that really wouldn’t happen otherwise.

Why does it matter?

“Now Rachael,” you may be saying, “your work definitely counts as STEM (science, technology, engineering and math). Why do you care so much about some humanities funding going away?”

I hope the reasons that I’ve outlined above help to make the point that humanities research has long-ranging impacts and is a good investment. NEH funding was pivotal in my development as a researcher. I would not be where I am today without early research experience on projects funded by the NEH.  And as a scholar working in multiple disciplines, I see how humanities research constantly enriches work in other fields, like engineering, which tend to be considered more desirable.

One final point: the National Endowment for the Humanities is, compared to other federal funding programs, very small indeed. In 2015 the federal government spent 146 million on the NEH, which was only 2% of the 7.1  billion dollar Department of Defense research budget. In other words, if everyone in the US contributed equally to the federal budget, the NEH would cost us each less than fifty cents a year. I think that’s a fair price for all of the different on-going projects the NEH funds, don’t you?

agencies3b

The entire National Endowment for the Humanities & National Endowment for the Arts, as well as the National Park Service research budget, all fit in that tiny “other” slice at the very top.

 

Acoustics Documentaries on Netflix

Happy New Year’s Eve! Have you made any resolutions? Perhaps a resolution to learn something new in the new year? If so, you’re in luck! I’ve recently run across a number of different Netflix documentaries that touch on differents aspects of acoustics that readers of this blog might enjoy. (Yes, I’ve spent a lot of my winter break watching documentaries. Why do you ask?)

Netflix-new-icon

Sure, I guess they have, like, movies and stuff, but really I’m here for the documentaries.

  • Sanrachna (Hindi with English subtitles)
    • This series focuses on the architecture of ancient India. The second episode is all about the architectural acoustics of Golconda fort and Gol Gumbaz. Through careful design and construction, a handclap in the foyer of Golconda fort can be heard half a mile away!
  • The Lion in Your Living Room (English)
    • This Canadian documentary is about domestic house cats. In addition to some discussion of the ins and outs of cat’s ears, there’s a really cool segment by Karen McComb where she talks about the acoustic qualities of different types of purrs.
    • Bonus: Some sweet examples of the Canadian vowel shift.
  • Ocean Giants (English)
    • This BBC documentary about whales and dolphins has three hour-long episodes, and each includes a lot of underwater acoustics and animal communication. If you’ve only got time for one episode, the third episode “Voices of the Sea” is all about whale and dolphin vocalizations.
  • Do I sound gay? (English)
    • This documentary by David Thorpe explores the stereotype of “a gay voice” and does include some cameos by linguists. From a sociolinguisitcs standpoint, I think it’s a bit simplistic (to be fair, probably becuase I’m a sociolinguist) but it’s still an interesting discussion of speech and identity.
    • Bonus: If you want to get a more linguistics-y perspective, this post on Language Log (and the comments) go into a lot of depth.

Oh, and if you don’t have Netflix, I’ve got you covered too. Here are two Youtube channels with linguistics contents you might like:

  • Lingthusiasm (English)
    • This is a brand-new podcast by Gretchen McCulloch and Lauren Gawne (two of my favorite internet linguistics people), and it’s a ton of fun. You should check it out!
  • The Ling Space (English)
    • This channel has been around for a while and has little bite-sized videos about a range of linguistics topics. They have a new video every Wednesday.

Do you know of any other good documentaries about linguistics or acoustics? Leave a comment and let me know!

Do emojis have their own syntax?

So a while ago I got into a discussion with someone on Twitter about whether emojis have syntax. Their original question was this:

As someone who’s studied sign language, my immediate thought was “Of course there’s a directionality to emoji: they encode the spatial relationships of the scene.” This is just fancy linguist talk for: “if there’s a dog eating a hot-dog, and the dog is on the right, you’re going to use 🌭🐕, not 🐕🌭.” But the more I thought about it, the more I began to think that maybe it would be better not to rely on my intuitions in this case. First, because I know American Sign Language and that might be influencing me and, second, because I am pretty gosh-darn dyslexic and I can’t promise that my really excellent ability to flip adjacent characters doesn’t extend to emoji.

So, like any good behavioral scientist, I ran a little experiment. I wanted to know two things.

  1. Does an emoji description of a scene show the way that things are positioned in that scene?
  2. Does the order of emojis tend to be the same as the ordering of those same concepts in an equivalent sentence?

As it turned out, the answers to these questions are actually fairly intertwined, and related to a third thing I hadn’t actually considered while I was putting together my stimuli (but probably should have): whether there was an agent-patient relationship in the photo.

Agent: The entity in a sentence that’s affecting a changed, the “doer” of the action.

  • The dog ate the hot-dog.
  • The raccoons pushed over all the trash-bins.

Patient: The entity that’s being changed, the “receiver” of the action.

  • The dog ate the hot-dog.
  • The raccoons pushed over all the trash-bins.

Data

To get data, I showed people three pictures and asked them to “pick the emoji sequence that best describes the scene” and then gave them two options that used different orders of the same emoji. Then, once they were done with the emoji part, I asked them to “please type a short sentence to describe each scene”. For all the language data, I just went through and quickly coded the order that the same concepts as were encoded in the emoji showed up.

Examples:

  • “The dog ate a hot-dog”  -> dog hot-dog
  • “The hot-dog was eaten by the dog” -> hot-dog dog
  • “A dog eating” -> dog
  • “The hot-dog was completely devoured” -> hot-dog

So this gave me two parallel data sets: one with emojis and one with language data.

All together, 133 people filled out the emoji half and 127 people did the whole thing, mostly in English (I had one person respond in Spanish and I went ahead and included it). I have absolutely no demographics on my participants, and that’s by design; since I didn’t go through the Institutional Review Board it would actually be unethical for me to collect data about people themselves rather than just general information on language use. (If you want to get into the nitty-gritty this is a really good discussion of different types of on-line research.)

Picture one – A man counting money

Watch, movie schedule, poster, telephone, cashier machine, cash register Fortepan 6680

I picked this photo as sort of a sanity-check: there’s no obvious right-to-left ordering of the man and the money, and there’s one pretty clear way of describing what’s going on in this scene. There’s an agent (the man) and a patient (the money), and since we tend to describe things as agent first, patient second I expected people to pretty much all do the same thing with this picture. (Side note: I know I’ve read a paper about the cross-linguistic tendency for syntactic structures where the agent comes first, but I can’t find it and I don’t remember who it’s by. Please let me know if you’ve got an idea what it could be in the comments–it’s driving me nuts!)

manmoney

And they did! Pretty much everyone described this picture by putting the man before the money, both with emoji and words. This tells us that, when there’s no information about orientation you need to encode (e.g. what’s on the right or left), people do tend to use emoji in the same order as they would the equivalent words.

Picture two – A man walking by a castle

Château de Canisy (5)

But now things get a little more complex. What if there isn’t a strong agent-patient relationship and there is a strong orientation in the photo? Here, a man in a red shirt is walking by a castle, but he shows up on the right side of the photo. Will people be more likely to describe this scene with emoji in a way that encodes the relationship of the objects in the photo?

mancastle

I found that they were–almost four out of five participants described this scene by using the emoji sequence “castle man”, rather than “man castle”. This is particularly striking because, in the sentence writing part of the experiment, most people (over 56%) wrote a sentence where “man/dude/person etc.” showed up before “castle/mansion/chateau etc.”.

So while people can use emoji to encode syntax, they’re also using them to encode spatial information about the scene.

Picture three – A man photographing a model

Photographing a model

Ok, so let’s add a third layer of complexity: what about when spatial information and the syntactic agent/patient relationships are pointing in opposite directions? For the scene above, if you’re encoding the spatial information then you should use an emoji ordering like “woman camera man”, but if you’re encoding an agent-patient relationship then, as we saw in the picture of the man counting money, you’ll probably want to put the agent first: “man camera woman”.

(I leave it open for discussion whether the camera emoji here is representing a physical camera or a verb like “photograph”.)

mangirlcamera

For this chart I removed some data to make it readable. I kicked out anyone who picked another ordering of the emoji, and any word order that fewer than ten people (e.g. less than 10% of participants) used.

So people were a little more divided here. It wasn’t quite a 50-50 split, but it really does look like you can go either way with this one. The thing that jumped out at me, though, was how the word order and emoji order pattern together: if your sentence is something like “A man photographs a model”, then you are far more likely to use the “man camera woman” emoji ordering. On the other hand, if your sentence is something like “A woman being photographed by the sea” or “Photoshoot by the water”, then it’s more likely that your emoji ordering described the physical relation of the scene.

So what?

So what’s the big takeaway here? Well, one thing is that emoji don’t really have a fixed syntax in the same way language does. If they did, I’d expect that there would be a lot more agreement between people about the right way to represent a scene with emoji. There was a lot of variation.

On the other hand, emoji ordering isn’t just random either. It is encoding information, either about the syntactic/semantic relationship of the concepts or their physical location in space. The problem is that you really don’t have a way of knowing which one is which.

Edit 12/16/2016: The dataset and the R script I used to analyze it are now avaliable on Github.

What’s the difference between & and +?

So if you’re like me, you sometimes take notes on the computer and end up using some shortcuts so you can keep up with the speed of whoever’s talking. One of the short cuts I use a lot is replacing the word “and” with punctuation. When I’m handwriting things I only ever use “+” (becuase I can’t reliably write an ampersand), but in typing I use both “+” and “&”. And I realized recently, after going back to change which one I used, that I had the intuition that they should be used for different things.

Ampersand-handwriting-3.png

I don’t use Ampersands when I’m handwriting things becuase they’re hard to write.

Like sometimes happens with linguistic intuitions, though, I didn’t really have a solid idea of how they were different, just that they were. Fortunately, I had a ready-made way to figure it out. Since I use both symbols on Twitter quite a bit, all I had to do was grab tweets of mine that used either + or & and figure out what the difference was.

I got 450 tweets from between October 7th and November 11th of this year from my own account (@rctatman). I used either & or + in 83 of them, or roughly 18%. This number is a little bit inflated because I was livetweeting a lot of conference talks in that time period, and if a talk has two authors I start every livetweet from that talk with “AuthorName1 & AuthorName2:”. 43 tweets use & in this way. If we get rid of those, only around 8% of my tweets contain either + or &. They’re still a lot more common in my tweets than in writing in other genres, though, so it’s still a good amount of data.

So what do I use + for? See for yourself! Below are all the things I conjoined with + in my Twitter dataset. (Spelling errors intact. I’m dyslexic, so if I don’t carefully edit text—and even sometimes when I do, to my eternal chagrin—I tend to have a lot of spelling errors. Also, a lot of these tweets are from EMNLP so there’s quite a bit of jargon.)

  • time + space
  • confusable Iberian language + English
  • Data + code
  • easy + nice
  • entity linking + entity clustering
  • group + individual
  • handy-dandy worksheet + tips
  • Jim + Brenda, Finn + Jake
  • Language + action
  • linguistic rules + statio-temporal clustering
  • poster + long paper
  • Ratings + text
  • static + default methods
  • syntax thing + cattle
  • the cooperative principle + Gricean maxims
  • Title + first author
  • to simplify manipulation + preserve struture

If you’ve had some syntactic training, it might jump out to you that most of these things have the same syntactic structure: they’re noun phrases! There are just a couple of exception. The first is “static + default methods”, where the things that are being conjoined are actually adjectives modifying a single noun. The other is “to simplify manipulation + preserve struture”. I’m going to remain agnostic about where in the verb phrase that coordination is taking place, though, so I don’t get into any syntax arguments ;). That said, this is a fairly robust pattern! Remember that I haven’t been taught any rules about what I “should” do, so this is just an emergent pattern.

Ok, so what about &? Like I said, my number one use is for conjunction of names. This probably comes from my academic writing training. Most of the papers I read that use author names for in-line citations use an & between them. But I do also use it in the main body of tweets. My use of & is a little bit harder to characterize, so I’m going to go through and tell you about each type of thing.

First, I use it to conjoin user names with the @ tag. This makes sense, since I have a strong tendency to use & with names:

  • @uwengineering & @uwnlp
  • @amazon @baidu @Grammarly & @google

In some cases, I do use it in the same way as I do +, for conjoining noun phrases:

  • Q&A
  • the entities & relations
  • these features & our corpus
  • LSTM & attention models
  • apples & concrete
  • context & content

But I also use it for comparatives:

  • Better suited for weak (bag-level) labels & interpretable and flexible
  • easier & faster

And, perhaps more interestingly, for really high-level conjugation, like at the level of the sentence or entire verb phrase (again, I’m not going to make ANY claims about what happens in and around verbs—you’ll need to talk to a syntactician for that!).

  • Classified as + or – & then compared to polls
  • in 30% of games the group performance was below average & in 17% group was worse than worst individual
  • math word problems are boring & kids learn better if they’re interested in the theme of the problem
  • our system is the first temporal tagger designed for social media data & it doesn’t require hand tagging
  • use a small labeled corpus w/ small lexicon & choose words with high prob. of 1 label

And, finally, it gets used in sort of miscellaneous places, like hashtags and between URLs.

So & gets used in a lot more places than + does. I think that this is probably because, on some subconscious level I consider & to be the default (or, in linguistics terms, “unmarked“). This might be related to how I’m processing these symbols when I read them. I’m one of those people who hears an internal voice when reading/writing, so I tend to have canonical vocalizations of most typed symbols. I read @ as “at”, for example, and emoticons as a prosodic beat with some sort of emotive sound. Like I read the snorting emoji as the sound of someone snorting. For & and +, I read & as “and” and + as “plus”. I also use “plus” as a conjunction fairly often in speech, as do many of my friends, so it’s possible that it may pattern with my use in speech (I don’t have any data for that, though!). But I don’t say “plus” nearly as often as I say “and”. “And” is definitely the default and I guess that, by extension, & is as well.

Another thing that might possibly be at play here is ease of entering these symbols. While I’m on my phone they’re pretty much equally easy to type, on a full keyboard + is slightly easier, since I don’t have to reach as far from the shift key. But if that were the only factor my default would be +, so I’m fairly comfortable claiming that the fact that I use & for more types of conjunction is based on the influence of speech.

A BIG caveat before I wrap up—this is a bespoke analysis. It may hold for me, but I don’t claim that it’s the norm of any of my language communities. I’d need a lot more data for that! That said, I think it’s really neat that I’ve unconsciously fallen into a really regular pattern of use for two punctuation symbols that are basically interchangeable. It’s a great little example of the human tendency to unconsciously tidy up language.