Analysing Languages in the New York Twittersphere

Following the interest in our Twitter language map of London a few months back, James Cheshire and I have been working on expanding our horizons a bit.  This time teaming up with John Barratt at Trendsmap, our new map looks at the Twitter languages of New York, New York!  This time mapping 8.5 million tweets, captured between January 2010 and February 2013.

Without further ado, here is the map. You can also find a fully zoomable, interactive version at, courtesy of the technical wizardry of Ollie O’Brien.


James has blogged over on Spatial Analysis about the map creation process and highlighted some of the predominant trends observed on the map.  What I thought I’d do is have a bit more of a deeper look into the underlying language trends, to see if slightly different visualisation techniques provide us with any alternative insight, and the data handling process.

Spatial Patterns of Language Density

Further to the map, I’ve had a more in-depth look at how tweet density and multilingualism varies spatially across New York.  Breaking New York down into points every 50 metres, I wrote a simple script (using Java and Geotools) that analysed tweet patterns within a 100 metre radius of each point.  These point summaries are then converted in a raster image – a collection of grid squares – to provide an alternative representation of spatial variation in tweeting behaviour.

Looking at pure tweet density to begin with, we take all languages into consideration.  From this map it is immediately clear how Manhattan dominates as the centre for Twitter activity in New York.  Yet we can also see how tweeting is far from constrained to this area, spreading out to areas of Brooklyn, Jersey City and Newark.  By contrast, little Twitter activity is found in areas like Staten Island and Yonkers.

Analysing Languages in the New York Twittersphere

Density of tweets per grid square (Coastline courtesy of ORNL)

By the same token, we can look at how multilingualism varies across New York, by identifying the number of languages within each grid square.  And we actually get a slightly different pattern.  Manhattan dominates again, but with a particularly high concentration in multilingualism around the Theatre District and Times Square – predominantly tourists, one presumes.  Other areas, where tweet density is otherwise high – such as Newark, Jersey City and the Bronx – see a big drop off where it comes to the pure number of languages being spoken.

Analysing Languages in the New York Twittersphere

Number of languages per 50m grid square (Coastline courtesy of ORNL)

Finally, taking this a little bit further, we can look at how multilingualism varies with respect to English language tweets.  Mapping the percentage of non-English tweets per grid square, we begin to get a sense of the areas of New York less dominated by the English language, and remove the influence of simply tweet density.  The most prominent locations, according to this measure, are now shown to be South Brooklyn, Coney Island, Jackson Heights and (less surprisingly) Liberty Island.  It is also interesting to see how Manhattan pretty much drops off the map here – it seems there are lots of tweets sent from Manhattan, but by far the majority are sent in English.

Analysing Languages in the New York Twittersphere

Percentage of non-English tweets per 50m grid square (Coastline courtesy of ORNL)

 Top Languages

So, having viewed the maps, you might now be thinking, ‘Where’s my [insert your language here]?’.  Well, check out this list, the complete set of languages ranked by count.  If your language still isn’t there then maybe you should go to New York and tweet something.

As you will see from the list, in common with London, English really dominates in the New York Twittersphere, making up almost 95% of all tweets sent.  Spanish fares well in comparison to other languages, but still only makes up 2.7% of the entire dataset.  Clearly, you wouldn’t expect the Twitter dataset to represent anything close to real-world interactions, but it would be interesting to hear from any New Yorkers (or linguists) about their interpretation of the rankings and volumes of tweets in each language.

Language Processing

Finally, a small word on the data processing front.  Keen readers will be aware that in the course of conducting the last Twitter language analysis, we experienced a pesky problem with Tagalog.  Not that I have a problem the language per se, but I refused to believe that it was the third most popular language in London.  The issue was to do with a quirk of the Google Compact Language Detector, and specifically its treatment of ‘hahaha’s and ‘lolololol’s and the like.  For this new analysis – working work with John Barratt and the wealth of data afforded to us by Trendsmap – we’ve increased the reliability of the detection, removing tweets less than 40 characters, @ replies and anything Trendsmap has already identified as spam.  So long, Tagalog.


Detecting Languages in London’s Twittersphere

Over the last couple of weeks, and as a bit of a distraction from finishing off my PhD, I’ve been working with James Cheshire looking at the use of different languages within my aforementioned dataset of London tweets.

I’ve been handling the data generation side, and the method really is quite simple.  Just like some similar work carried out by Eric Fischer, I’ve employed the Chromium Compact Language Detector – a open-source Python library adapted from the Google Chrome algorithm to detect a website’s language – in detecting the predominant language contained within around 3.3 million geolocated tweets, captured in London over the course of this summer.

James has mapped up the data – shown below, or in zoomable form here – and he more fully describes some of the interesting trends that may be observed over on his blog.

Detecting Languages in London's Twittersphere

With respect to the detection process, the CLD tool appears to work pretty well.  In total, 66 languages were detected among the complete dataset (including a bit of Basque, Haitian Creole and Swahili, surprisingly enough), and on the whole these classifications appear to be correct.  In cases where the tool is not completely confident in what is it reading – usually due to the brevity or colloquiality of a tweet – classification is marked as unknown or unreliable, and in these cases we end up losing around 1.4 million of additional tweets.

One issue with this approach that I did note was the surprising popularity of Tagalog, a language of the Philippines, which initially was identified as the 7th most tweeted language.  On further investigation, I found that many of these classifications included just uses of English terms such as ‘hahahahaha’, ‘ahhhhhhh’ and ‘lololololol’.  I don’t know much about Tagalog but it sounds like a fun language.  Nevertheless, Tagalog was excluded from our analysis.

I won’t dwell too much on discussing the results, only that Twitter appears to reveal itself here to be the severely skewed dataset we all always really knew it was.  In total, 92.5% of tweets are detected as English, far above existing estimations (60%) of English speakers in London.  While languages you’d expect to score highly – such as Bengali and Somali – barely feature at all.  Either people only tweet in English, or usage of Twitter varies significantly among language groups in London.  There is a great deal you can say about bias within the Twitter dataset, but I think I’ll save that for another day.

For the time being, enjoy the map.