Last week I wrote a post called “If you’re not a linguist, don’t do linguistics”. This got shared around Twitter quite a bit and made it to the front page of r/linguistics, so a lot of people saw it. Pretty much everyone had good insight on the topic and it generated some great discussion. I thought it would be good to write a follow-up to flesh out my main concerns in a more serious manner (this time sans emoticons!) and to address the concerns some people had with my reasoning.

The paper in question is by Dodds et al. (2015) and it is called “Human language reveals a universal positivity bias”. The certainty of that title is important since I’m going to try to show in this post that the authors make too many assumptions to reliably make any claims about all human language. I’m going to focus on the English data because that is what I am familiar with. But if anyone who is familiar with the data in other languages would like to weigh in, please do so in the comments.

The first assumption made by the authors is that it is possible to make universal claims about language using only written data. This is not a minor issue. The differences between spoken and written language are many and major (Linell 2005). But dealing with spoken data is difficult – it takes much more time and effort to collect and analyze than written data. We can argue, however, that even in highly literate societies, the majority of language use is spoken – and spoken language does not work like written language. This is an assumption that no scholar should ever make. So any research which makes claims about all human language will therefore have to include some form of spoken data. But the data set that the authors draw from (called their corpus) is made from tweets, song lyrics, New York Times articles and the Google Books project. Tweets and song lyrics, let alone news articles or books, do not mimic spoken language in an accurate way. For example, these registers may include the same words as human speech, but certainly not in the same proportion. Written language does not include false starts, nor does it include repetition or elusion in near the same way that spoken language does. Anyone who has done any transcription work will tell you this.

The next assumption made by the authors is that their data is representative of all human language. Representativeness is a major issue in corpus linguistics. When linguists want to investigate a register or variety of language, they build a corpus which is representative of that register or variety by taking a large enough and balanced sample of texts from that register. What is important here, however, is that most linguists do not have a problem with a set of data representing a larger register – so long as that larger register isn’t all human language. For example, if we wanted to research modern English journalism (quite a large register), we would build a corpus of journalism texts from English-speaking countries and we would be careful to include various kinds of journalism – op-eds, sports reporting, financial news, etc. We would not build a corpus of articles from the Podunk Free Press and make claims about all English journalism. But representativeness is a tricky issue. The larger the language variety you are trying to investigate, the more data from that variety you will need in your corpus. Baker (2010: 7) notes that a corpus analysis of one novel is “unlikely to be representative of all language use, or all novels, or even the general writing style of that author”. The English sub-corpora in Dodds et al. exists somewhere in between a fully non-representative corpus of English (one novel) and a fully representative corpus of English (all human speech and writing in English). In fact, in another paper (Dodds et al. 2011), the representativeness of the Twitter corpus is explained as “First, in terms of basic sampling, tweets allocated to data feeds by Twitter were effectively chosen at random from all tweets. Our observation of this apparent absence of bias in no way dismisses the far stronger issue that the full collection of tweets is a non-uniform subsampling of all utterances made by a non-representative subpopulation of all people. While the demographic profile of individual Twitter users does not match that of, say, the United States, where the majority of users currently reside, our interest is in finding suggestions of universal patterns.”. What I think that doozy of a sentence in the middle is saying is that the tweets come from an unrepresentative sample of the population but that the language in them may be suggestive of universal English usage. Does that mean can we assume that the English sub-corpora (specifically the Twitter data) in Dodds et al. is representative of all human communication in English?

Another assumption the authors make is that they have sampled their data correctly. The decisions on what texts will be sampled, as Tognini-Bonelli (2001: 59) points out, “will have a direct effect on the insights yielded by the corpus”. Following Biber (see Tognini-Bonelli 2001: 59), linguists can classify texts into various channels in order to assure that their sample texts will be representative of a certain population of people and/or variety of language. They can start with general “channels” of the language (written texts, spoken data, scripted data, electronic communication) and move on to whether the language is private or published. Linguists can then sample language based on what type of person created it (their age, sex, gender, social-economic situation, etc.). For example, if we made a corpus of the English articles on Wikipedia, we would have a massive amount of linguistic data. Literally billions of words. But 87% of it will have been written by men and 59% of it will have been written by people under the age of 40. Would you feel comfortable making claims about all human language based on that data? How about just all English language encyclopedias?

The next assumption made by the authors is that the relative positive or negative nature of the words in a text are indicative of how positive that text is. But words can have various and sometimes even opposing meanings. Texts are also likely to contain words that are written the same but have different meanings. For example, the word fine in the Dodds et al. corpus, like the rest of the words in the corpus, is just a four letter word – free of context and naked as a jaybird. Is it an adjective that means “good, acceptable, or satisfactory”, which Merriam-Webster says is sometimes “used in an ironic way to refer to things that are not good or acceptable”? Or does it refer to that little piece of paper that the Philadelphia Parking Authority is so (in)famous for? We don’t know. All we know is that it has been rated 6.74 on the positivity scale by the respondents in Dodds et al. Can we assume that all the uses of fine in the New York Times are that positive? Can we assume that the use of fine on Twitter is always or even mostly non-ironic? On top of that, some of the most common words in English also tend to have the most meanings. There are 15 entries for get in the Macmillan Dictionary, including “kill/attack/punish” and “annoy”. Get in Dodds et al. is ranked on the positive side of things at 5.92. Can we assume that this rating carries across all the uses of get in the corpus? The authors found approximately 230 million unique “words” in their Twitter corpus (they counted all forms of a word separately, so banana, bananas, b-a-n-a-n-a-s! would be separate “words”; and they counted URLs as words). So they used the 50,000 most frequent ones to estimate the information content of texts. Can we assume that it is possible to make an accurate claim about how positive or negative a text is based on nothing but the words taken out of context?

Another assumption that the authors make is that the respondents in their survey can speak for the entire population. The authors used Amazon’s Mechanical Turk to crowdsource evaluations for the words in their sub-corpus. 60% of the American people on Mechanical Turk are women and 83.5% of them are white. The authors used respondents located in the United States and India. Can we assume that these respondents have opinions about the words in the corpus that are representative of the entire population of English speakers? Here are the ratings for the various ways of writing laughter in the authors’ corpus:

And here is a picture of a character expressing laughter:

Can we assume that the textual representation of laughter is always as positive as the respondents rated it? Can we assume that everyone or most people on Twitter use the various textual representations of laughter in a positive way – that they are laughing with someone and not at someone?
Finally, let’s compare some data. The good people at the Corpus of Contemporary American English (COCA) have created a word list based on their 450 million word corpus. The COCA corpus is specifically designed to be large and balanced (although the problem of dealing with spoken language might still remain). In addition, each word in their corpus is annotated for its part of speech, so they can recognize when a word like state is either a verb or a noun. This last point is something that Dodds et al. did not do – all forms of words that are spelled the same are collapsed into being one word. The compilers of the COCA list note that “there are more than 140 words that occur both as a noun and as a verb at least 10,000 times in COCA”. This is the type/token issue that came up in my previous post. A corpus that tags each word for its part of speech can tell the difference between different types of the “same” word (state as a verb vs. state as a noun), while an untagged corpus treats all occurrences of state as the same token. If we compare the 10,000 most common words in Dodds et al. to a sample of the 10,000 most common words in COCA, we see that there are 121 words on the COCA list but not the Dodds et al. list (Here is the spreadsheet from the Dodds et al. paper with the COCA data – pnas.1411678112.sd01 – Dodds et al corpus with COCA). And that’s just a sample of the COCA list. How many more differences would there be if we compared the Dodds et al. list to the whole COCA list?

To sum up, the authors use their corpus of tweets, New York Times articles, song lyrics and books and ask us to assume (1) that they can make universal claims about language despite using only written data; (2) that their data is representative of all human language despite including only four registers; (3) that they have sampled their data correctly despite not knowing what types of people created the linguistic data and only including certain channels of published language; (4) that the relative positive or negative nature of the words in a text are indicative of how positive that text is despite the obvious fact that words can be spelled the same and still have wildly different meanings; (5) that the respondents in their survey can speak for the entire population despite the English-speaking respondents being from only two subsets of two English-speaking populations (USA and India); and (6) that their list of the 10,000 most common words in their corpus (which they used to rate all human language) is representative despite being uncomfortably dissimilar to a well-balanced list that can differentiate between different types of words.

I don’t mean to sound like a Negative Nancy and I don’t want to trivialize the work of the authors in this paper. The corpus that they have built is nothing short of amazing. The amount of feedback they got from human respondents on language is also impressive (to say the least). I am merely trying to point out what we can and can not say based on the data. It would be nice to make universal claims about all human language, but the fact is that even with millions and billions of data points, we still are not able to do so unless the data is representative and sampled correctly. That means it has to include spoken data (preferably a lot of it) and it has to be sampled from all socio-economic human backgrounds.

Hat tip to the commenters on the last post and the redditors over at r/linguistics.

References

Dodds, Peter Sheridan, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, and Christopher M. Danforth. 2015. “Human language reveals a universal positivity bias”. PNAS 112:8. http://www.pnas.org/content/112/8/2389

Dodds, Peter Sheridan, Kameron Decker Harris, Isabel M. Koumann, Catherine A. Bliss, Christopher M. Danforth. 2011. “Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter”. PLOS One. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026752#abstract0

Baker, Paul. 2010. Sociolinguistics and Corpus Linguistics. Edinburgh: Edinburgh University Press. http://www.ling.lancs.ac.uk/staff/paulb/socioling.htm

Linell, Per. 2005. The Written Language Bias in Linguistics. Oxon: Routledge.

Mair, Christian. 2015. “Responses to Davies and Fuchs”. English World-Wide 36:1, 29–33. doi: 10.1075/eww.36.1.02mai

Tognini-Bonelli, Elena. 2001. Studies in Corpus Linguistics, Volume 6: Corpus Linguistics as Work. John Benjamins. https://benjamins.com/#catalog/books/scl.6/main