Understanding the content of Tweets is important for many reasons: grasping a user’s interests (which in turn lets us show more relevant content), improving search, and fighting spam. There are many steps involved in a typical natural language processing pipeline, but one of the first and most fundamental steps is language identification — determining the language in which a piece of text is written.
This is generally not a hard problem. Even with a small and simple model (e.g., list of most common words in each language), we can achieve near-perfect accuracy when classifying news articles , for example. However, Tweets are different from the average news article or web page: they’re very short and use informal language. In practice, they’re different enough that we don’t want to evaluate language classifiers on news articles, because that doesn’t tell us much about their performance on Tweets. What we need, then, is a golden set of language-annotated Tweets to evaluate on. So, how did we go about constructing it?
To measure performance on Twitter overall, we can simply take a uniform sample of all Tweets and manually annotate it.
The problem is that each annotator can recognize only one or two languages, and it’s prohibitively inefficient and expensive to have every annotator look at every Tweet. Therefore, we annotated every Tweet with three independent software packages (see Semi-automatic annotation, below) and then kept the majority language as “likely.” All Tweets with likely language X were given to native speakers of X to annotate. The annotators were instructed to either enter the true language (if they recognized it) or skip the Tweet. For all skipped Tweets, we then determined the second most-likely language and gave the Tweet to annotators again. For the Tweets that got skipped even in the second round, we manually investigated the user’s profile and used internet resources (dictionaries, search, etc.) to determine the true language. For 0.18% of Tweets, we were unable to determine the language; because it’s a tiny number, we simply discarded them.
In total, we ended up with 120,575 annotated Tweets. This uniformly sampled dataset is available for download (see below).
The uniformly sampled dataset theoretically allows us to measure overall accuracy, as well as precision and recall for each language. However, we can detect over 60 languages, and only 17 of them have at least 100 Tweets in this dataset. The fewer Tweets we have in a certain language, the less we can say about that language’s performance. For example, based on just five Czech Tweets, it is impossible to say with any reasonable confidence what the actual precision and recall are of our Czech classifier. We therefore built two more datasets.
We construct a dataset for each language separately. Let’s take German as an example; we follow the same procedure for all languages.
To measure recall of the German classifier, we need an unbiased  sample of all Tweets in German. We cannot simply task a human annotator to go through all Tweets, as they would need to read hundreds of thousands of English Tweets to gather 1000 German ones . Instead, we use the following heuristic procedure:
We call this the recall-oriented dataset. The data collection strategy is obviously not uniform; how certain can we be that it provides an accurate estimate of recall? For the 10 biggest languages, we measured our classifier’s recall on three datasets:
The differences in recall estimates were statistically indistinguishable using a 95% confidence interval . For smaller languages, we don’t always have enough data to rely on geo tags or the uniform stream, which is why we used the “10% heuristic” outlined above for all languages. While our 3-way comparison test on the 10 big languages does not strictly guarantee that the recall-oriented dataset is a good recall estimator for small languages as well, it did assuage our concerns significantly.
To get even better recall estimates for the large languages, we suggest using the union of the uniformly sampled dataset and the recall-oriented dataset.
Measuring precision (on the full-Twitter language distribution) is trivial. Grab, say, one thousand random Tweets for which your classifier (let’s call it v1) triggers, and have them human-annotated with two labels: German or non-German. We call this the precision-oriented dataset, and we can use it on its own or union it with the uniformly sampled dataset (to get even better estimates for the large languages).
A major caveat is that if we later want to evaluate a different algorithm (let’s call it v2; it could be an improved variant of v1, or a completely different algorithm), no amount of tweaking our existing data can give us the true performance numbers. Consider the following Venn diagram:
Our precision-oriented dataset samples the dotted area (i.e. the overlap between “predicted German v1” and “true German” well). But in the shaded area, (almost) all Tweets are not human-annotated, so we cannot say much about the precision there. We have a sample of all true German Tweets in our recall-oriented dataset, but the sample is so small (<0.0001%) that its intersection with “predicted German v2” is not informative.
A rough estimate of v2’s performance is to ignore the shaded part of the Venn diagram, and simply evaluate on the precision-oriented dataset created with v1. The more dissimilar v1 and v2 are, the rougher the estimate. Still, it’s better than nothing, which is why we’re also releasing the prediction-oriented dataset created with an old version of Twitter’s language identifier.
Another use of the recall-oriented dataset is to measure precision on hypothetical data with a balanced language distribution (i.e., the same number of Tweets for every language). While not a commonly encountered setting in real life , it is the “fairest,” most use-case agnostic way of comparing disparate classifiers. It is used for example by Mike McCandless and the author of the “language-detection” java package. However, in practice it is more common to evaluate precision on datasets with a real-world skew from some specific use case; our equivalent of that is the uniformly sampled dataset. A good overview paper is Cross-domain Feature Selection for Language Identification (2011) by Marco Lui (@saffsd) and Timothy Baldwin (@eltimster); see also Evaluation of Language Identification Methods (2005) by Simon Kranig for older experiments.
Note that at world scale (all Tweets, all web pages, etc.), the language distribution is extremely skewed, and measuring precision on the balanced (i.e., recall-oriented) dataset can give very deceiving results. For example, our internal classifier at Twitter labels English Tweets with 99% precision, but on the recall-oriented dataset, its precision is 70%! That’s because the classifier over-triggers on some smaller languages (e.g., Dutch). Those errors are negligible in the wild, because we see very few Dutch Tweets compared to English ones; but in the recall-oriented dataset, Dutch Tweets represent roughly 1/68 of all data, just like English and the other 66 languages.
Language distribution skew is also the reason we cannot reuse the recall-oriented dataset to measure precision even with reweighting. For example, if English, German, and Slovenian Tweets appeared at a ratio of 10:3:1, we could create 10 virtual copies of every golden English Tweet, and similarly for German, then evaluate. However, this approach breaks down when we realise that the ratio English:Slovenian is closer to 500:1. Imagine that in our recall-oriented dataset, which has about 1500 Tweets per language, the Slovenian classifier triggers on 1200 Slovenian Tweets and a single English Tweet. Taking the 95% confidence interval, we can conclude that between 0.007% and 0.311% of all English Tweets get labeled as Slovenian. Now let’s apply the 500:1 weighting: the Slovenian classifier is expected to trigger on 1200 Slovenian Tweets and 0.007%*500*1500=52 to 2325 English Tweets. So Slovenian precision is estimated to be between 1200/(1200+52)=95.8% and 1200/(1200+2325)=34% — hardly useful. We neglected and simplified several details in this example, but the core problem remains the same.
Human annotation is always a hairy task with some inconsistency involved. For the majority of Tweets, it is not questionable what the main language is. But there are also a number of Tweets that are linguistically ambiguous or contain more than one language. To keep the complexity of the annotation task reasonable, we decided to use a single label for all such cases: “und” for “undefined” .
To make the labeling process as predictable and consistent as possible, the annotators were given the following instructions:
Please help us determine the language in which Tweets are written. Possible answers:
A language code "xx" (choose from list of possible codes). Choose this if a person HAS to speak xx to understand most of the Tweet or all of it, and speaking ONLY xx is enough to understand most or all of the Tweet.
Undefined. Choose this if any of the following applies:
• the Tweet can be interpreted in multiple languages
(words used by multiple languages, interjections ("haha!", "yay"), proper names, emoticons, ...)
• the Tweet strongly mixes languages and does not have a clear "main" language
• the Tweet is gibberish, not written in any language (e.g. "#HarryStyles alskdfbasfd")
• the Tweet is written in an actual language not available on our list. In this case, please mark this in the Comments column.
Leave empty. If you are unable to provide an answer (i.e. you think the Tweet is written in an actual language, but do not recognize the language), do not enter anything.
It's OK to leave rows empty if you don't recognize the language -- you should not need to spend more than 10 seconds on a Tweet, and usually much less.
We also presented annotators with the following borderline examples that were intended to calibrate them:
We replaced all at-mentions in Tweets with “@xzy” prior to presenting them to users, because usernames cannot be translated and do not inherently have a language. For example, we felt that “@common_squirrel Wow!” should not be labeled as English, because “wow” could come from a number of languages, and “@common_squirrel” will always be “@common_squirrel”, even in a German Tweet. In addition, usernames are limited to ASCII only. Conversely, we did not obfuscate hashtags as they are free-form (can be translated/adapted to other languages) and support all scripts.
We ran out of resources to evaluate inter-annotator agreement, and solicited only one label per Tweet. Because each language was annotated by a different set of annotators, and because languages vary in how unique/confusable they are, inter-annotator agreement would have to be measured for each language separately.
The more Tweets we can annotate, the smaller the error bars on our performance estimates. We therefore tried to expend human annotation resource on only those Tweets where it was not possible to very reliably determine the language automatically. Luckily, this turns out to be the minority of all Tweets.
For every Tweet that needed to be language-annotated, we first ran three independent langid algorithms on it: Twitter’s internal algorithm, Google’s CLD2 (https://code.google.com/p/cld2/), and langid.py (https://github.com/saffsd/langid.py). If they all assign the same language label, we assume this is the true label, without consulting human annotators.
Informal evaluation shows this “triple-agreement” method has <1% error rate (no errors detected in a few minutes’ scanning of output). The three algorithms agree on about two-thirds of all Tweets.
The annotated Tweets are available for download for anyone to evaluate their language identification algorithms:
In addition, precision_oriented.tsv contains language codes like
not-en, which indicates this tweet is not English, though we don’t know its actual language.
All Tweets are from July 2014 and cover 70 languages: am (Amharic), ar (Arabic), bg (Bulgarian), bn (Bengali), bo (Tibetan), bs (Bosnian), ca (Catalan), ckb (Sorani Kurdish), cs (Czech), cy (Welsh), da (Danish), de (German), dv (Maldivian), el (Greek), en (English), es (Spanish), et (Estonian), eu (Basque), fa (Persian), fi (Finnish), fr (French), gu (Gujarati), he (Hebrew), hi (Hindi), hi-Latn (Latinized Hindi), hr (Croatian), ht (Haitian Creole), hu (Hungarian), hy (Armenian), id (Indonesian), is (Icelandic), it (Italian), ja (Japanese), ka (Georgian), km (Khmer), kn (Kannada), ko (Korean), lo (Lao), lt (Lithuanian), lv (Latvian), ml (Malayalam), mr (Marathi), ms (Malay), my (Burmese), ne (Nepali), nl (Dutch), no (Norwegian), pa (Panjabi), pl (Polish), ps (Pashto), pt (Portuguese), ro (Romanian), ru (Russian), sd (Sindhi), si (Sinhala), sk (Slovak), sl (Slovenian), sr (Serbian), sv (Swedish), ta (Tamil), te (Telugu), th (Thai), tl (Tagalog), tr (Turkish), ug (Uyghur), uk (Ukrainian), ur (Urdu), vi (Vietnamese), zh-CN (Simplified Chinese), zh-TW (Traditional Chinese). There is a smattering of other language codes present in the data as an artifact of our labeling process, but Tweets in those languages were not collected systematically.
cat >/tmp/fetch.sh <<EOF
twurl "/1.1/statuses/lookup.json?id=$(echo $@ | tr ' ' ,)&trim_user=true" | jq -c ".|[.id_str, .text]"
cat uniformly_sampled.tsv | cut -f2 | xargs -n100 /tmp/fetch.sh > hydrated.json
Make sure you’ve completed the OAuth setup for twurl (see “Getting started” on their github page) before running the above commands. The code above observes the API rate limits by sleeping between requests. It silently skips Tweets that have been removed, and you will need to join the hydrated.json file with original language labels in uniformly_sampled.tsv.
Everyone on our team contributed to handling the annotation process, helped work out the methodology kinks, and occasionally labeled a tweet or two themselves. Big thanks to Gianna Badiali (@gianna), Eden Golshani (@edeng), Hohyon Ryu (@nlpenguin), Nathaniel Okun (@natoparkway), and Sumit Shah (@bigloser).
 An early paper by Grefenstette (1995) evaluates this simple technique on newspaper articles and for a limited set of European languages. They achieve near-perfect accuracy for the big European languages on sentences with 20+ words.
 Unbiased in the sense that German Tweets in our sample should be statistically indistinguishable from all German Tweets. They should have the same distribution of character ngram frequencies, word frequencies, emoticon and emoji usage, etc.
 And literally billions of English Tweets to gather 1,000 Tibetan ones.
 Interesting detail: We were originally afraid that the recall-oriented dataset might overestimate recall because it’s based on users that our own algorithm originally recognized as German, creating a possible positive feedback loop. Against our expectations, the recall as measured on the recall-oriented dataset is consistently slightly lower. We hypothesize that this is because of how Tweets were presented to annotators: when constructing the German recall-oriented dataset, all candidate Tweets were presented only to German annotators. For a borderline German/Swedish Tweet, it’s easy to imagine a sloppy German annotator to have a bias to yes and label it as German. Conversely, when constructing the uniformly sampled dataset, a borderline German/Swedish Tweet had a good chance of having been looked at by a Swedish annotator.
 A possible exception are environments with very few languages, e.g. a Canadian blog with a 60:40 English:French split in content. The recall-oriented dataset is well suited to estimating performance there.
 ISO-693-3 suggests more expressive special labels, and uses “und” to essentially mean “unlabeled”. This is the only place where we deviate slightly from the BCP-47 standard.