What you can, can’t and shouldn’t do with social media data

Earlier this summer, I gave a talk on the promise & pitfalls of social media data for the Joint Statistical Meetings. While I don’t think there’s a recording of the talk, enough people asked for one that I figured it would be worth putting together a blog post version of the talk. Enjoy!


What you can do with social media data

Let’s start with the good news: research using social media data has revolutionized social science research. It’s let us ask bigger question more quickly, helped us overcome some of the key drawbacks of behavioral experimental work and ask new kinds of questions.

More data faster

I can’t overstate how revolutionary the easy availability of social media data has been, especially in linguistics. It has increased both the rate and scale of data collection by orders of magnitude. Compare the time it took to compare the Dictionary of American Regional English (DARE) to the Wordmapper app below. The results are more or less the same, maps of where in the US folks use different words (in this example, “cellar”). But what once took the entire careers of multiple researchers can now be done in a few months, and with far higher resolution.

Dictionary of American Regional English (DARE) Word Mapper App
Data collection 48 years (1965 – 2013) <1 year
Size of team 2,777 people 4 people
Number of participants 1,843 people 20 million
DARE map Wordmapper Map

Social networks

Social media sites with a following or friend feature also let us ask really large scale questions about social networks. How do social networks and political affiliation interact? How does language change move through a social network? What characteristics of social network structure are more closely associated with the spread of misinformation? Of course, we could ask these questions before social media data… but by using APIs to access social media data, we reduce the timescale of these projects from decades to weeks or even days and we have a clear way to operationalize social network ties. It’s fairly hard for someone to sit down and list everyone they interact with face-to-face, but it’s very easy to grab a list of all the Twitter accounts you follow.

Wild-caught, all natural data

One of the constant struggles in experimental work is the fact that the mere fact of being observed changes behavior. This is known as the Hawthorne Effect in psychology or the Observer’s Paradox in sociolinguistics. As a result, even the most well-designed experiment is limited by the fact that the participants know that they are completing an experiment.

Social media data, however, doesn’t have this limitation. Since most social media research projects are conducted on public data without interacting directly with participants, they are not generally considered human subjects research. When you post something on a public social media account, you don’t have a reasonable expectation of privacy. In other words, you know that just anyone could come along and read it, and that includes researchers. As a result it is not generally necessary to collect informed consent for social media projects. (Informed consent is when you are told exactly what’s going to happen during an experiment you’re participating, and you agree to participate in it.) This means that the vast majority of folks who are participating in a social media study don’t actually know that they’re part of a study.

The benefit of this is that it allows researchers to get around three common confounds that plague social science research:

  • Bradley effect: People tend to tell researchers what they think they want to hear
  • Response bias: The sample of people willing to do an experiment/survey differ in a meaningful way from the population as a whole
  • Observer’s paradox/Hawthorne effect: People change their behavior when they know they’re being observed

While this is a boon to researchers, the lack of informed consent does introduce other other problems, which we’ll talk about later.

What you can’t do with social media data

Of course, all the benefits of social media come at a cost. There are several key drawbacks and limitations of social media research:

  • You can’t be sure who your participants are.
  • There’s inherent sampling bias.
  • You can’t violate the developer’s agreements.

You’re not sure who you’re studying…

Because you don’t meet with the people whose data is included in your study, you don’t know for sure what sorts of demographic categories they belong to, whether they are who they’re claiming to be or even if they’re human at all. You have to deal with both bots, accounts where content is produced and distributed automatically by a computer and sock puppets, where one person pretends to be another person. Sock puppets in particular can be very difficult to spot and may skew your results in unpredictable ways.

…but you can be sure your sample is biased.

Social media users aren’t randomly drawn from the world’s population as a whole. Social media users tend to be WEIRD: from wealthy, educated, industrialized, rich and democratic societies. This group is already over-represented in social science and psychology research studies, which may be subtly skewing our models of human behavior.

In addition, different social media platforms have different user bases. For example, Instagram and Snapchat tend to have younger users, Pinterest has more women (especially compared to Reddit, which skews male) and LinkedIn users tend to be highly educated and upper middle class. And that doesn’t even get to social network effects: you’re more likely to be on the same platform your friends are on, and since social networks tend to be homophilous, you can end up with pockets of very socially homogeneous folks. So, even if you manage to sample randomly from a social media platform, your sample is likely to differ from one taken from the population as a whole.

You need to abide by the developer’s agreements for whatever platform you’re using data from.

This is mainly an issue if you’re using API (application programmatic interface) to fetch data from a service. Developer’s agreements vary between platforms, but most limit the amount of data you can fetch and store, and how and if you can share it with other researchers. For example, if you’re sharing Twitter data you can only share 50,000 tweets at a time and even then only if you have to have people download a file by clicking on it. If you share any more than that, you should just share the ID’s of the tweets rather than the full tweets. (Document the Now’s Hydrator can help you fetch the tweets associated with a set of IDs.)

What you shouldn’t do with social media data

Finally, there are ethical restrictions on what we should do with social media data. As researchers, we need to 1) respect the wishes of users and 2) safeguard their best interests, especially given that we don’t (currently) generally get informed consent from the folks whose data we’re collecting.

Respecting users’ wishes

At least in the US, ethical human subjects research is led by three guiding principles set forth in the Belmont report. If you’re unfamiliar with the report, it was written in the aftermath of the Tuskegee Valley experiments. These were a series of medical experiments on African Americans men who had contracted syphilis conducted from the 1930’s to 1970’s. During the study, researchers withheld the cure (and even information that it existed) from the participants. The study directly resulted in the preventable deaths of 128 men and many health problems for study participants, their wives and children. It was a clear ethical violation of the human rights of participants and the moral stain of it continues to shape how we conduct human subjects research in the US.

The three principles of ethical human subjects research are:

  1. Respect for Persons: People should be treated as autonomous individuals and persons with diminished autonomy (like children or prisoners) are entitled to protection.
  2. Beneficence: 1) Do not harm and 2) maximize possible benefits and minimize possible harms.
  3. Justice: Both the risks and benefits of research should be distributed equally.

Social media research might not technically fall under the heading of human subjects research, since we aren’t intervening with our participants. However, I still believe that it’s important that researchers following these general guides when designing and distributing experiments.

One thing we can do is respect their wishes of the communities we study. Fortunately, we have some evidence of what those wishes are. Feisler and Proferes (2018) surveyed 368 Twitter users on their perception of a variety of research behaviors.

Screenshot from 2018-07-25 16-10-21
Fiesler, C., & Proferes, N. (2018). “Participant” Perceptions of Twitter Research Ethics. Social Media+ Society, 4(1), 2056305118763366. Table 4. 

In general, Twitter users are more OK with research with the following characteristics:

  • Large datasets
  • Analyzed automatically
  • Social media users informed about research
  • If tweets are quoted, they are anonymized. (Note that if you include the exact text, it’s possible to reverse search the quoted tweet and de-anonymize it. I recommend changing at least 20% of the content words in a tweet to synonyms to get around this and double-checking by trying to de-anonymize it yourself.)

These characteristics, however, are not as acceptable to Twitter users:

  • Small datasets
  • Analysis done by hand (presumably including analysis by Mechanical Turk workers)
  • Tweets from protected accounts or deleted tweets analyzed (which is also against the developer’s agreement, so you shouldn’t be doing this anyway)
  • Quoting with citation (very different from academic norms!)

In general, I think these suggest general best practices for researchers working with Twitter data.

  • Stick to larger datasets
  • Try to automate wherever possible
  • Follow the developer’s agreement
  • Take anonymity seriously.

There is one thing I disagree with, however: I don’t think we should contact everyone who’s tweets we use in our research.

Should we contact people whose tweets we use in our studies? My gut instinct on this one is “no”. If you’re collecting a large amount of data, you probably shouldn’t reach out to everyone in the data.

For users who don’t have open DM’s, the only way to contact them is to publicly mention them using @username. The problem with this is that it partly de-anonymizes your data. If you then choose to share your data, having publicly shared a list of whose data was included in the dataset it makes it much easier to de-anonymize. Instead of trying to figure out whose tweets were included when looking at all of Twitter, an adversary only has to figure out which of the users on the list you’ve given them is connected to which record.

The main exception to this is if have a project that’s a deep dive on one user, in which case you probably should. (For example, I contacted Chaz Smith and let him know about my phonological analysis of his #pronouncingthingsincorrectly Vines.)

Do no harm

Another aspect of ethical research is trying to ensure that your research or research data doesn’t have potentially unethical applications. The elephant in the room here, of course, is the data Cambridge Analytica collected from Facebook users. Researchers at Cambridge, collecting data for a research project, got lots of people’s permission to access their Facebook data. While that wasn’t a problem, they collected and saved Facebook data from other folks as well, who hadn’t opted in. In the end, only a half of a half of a percent of the folks whose data was in the final dataset actually agreed to be included in it. To make matters worse, this data was used by a commercial company founded by one of the researchers to (possibly) influence elections in the US and UK. Here’s a New York Times article that goes into much more detail. This has understandably lead to increased scrutiny of how social media research data is collected and used.

I’m not bringing this up to call out Facebook in particular, but to explain why it’s important to consider how research data might be used long-term. How and where will it be stored? For how long? Who will have access to it? In short, if you’re a researcher, how can you ensure that data you collected won’t end up somehow hurting the people you collected it from?

As an example of how important these questions are, consider this OK Cupid “research” dataset. It was collected without consent and shared publicly without anonymization. It included many personal details that were only intended to be shared with other users of the site, including explicit statements of sexual orientation. In addition to being an unforgivable breach of privacy, this directly endangered users whose data was collected: information on sexual orientation was shared for people living in countries where homosexuality is a crime that carries a death penalty or sentence of life in prison. I have a lot of other issues with this “study” as well, but the fact that it directly endangered research subjects who had no chance to opt out is by far the most egregious ethical breach.

If you are collecting social media data for research purposes, it is your ethical responsibility to safeguard the well-being of the people whose data you’re using.

I bring up these cautionary tales not to scare you off of social media research but to really impress the gravity of the responsibility you carry as a social media researcher. Social media data has the potential to dramatically improve our understanding of the world. A lot of my own work has relied heavily on it! But it’s important that we, as researchers, take our moral duty to make sure that we don’t end up doing more harm than good very seriously.

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Are emoji sequences as informative as text?

Something I’ve been thinking about a lot lately is how much information we really convey with emoji. I was recently at the 1​st​ International Workshop on Emoji Understanding and Applications in Social Media and one theme that stood out to me from the papers was that emoji tend to be used more to communicate social meaning (things like tone and when a conversation is over) than semantics (content stuff like “this is a dog” or “an icecream truck”).

I’ve been itching to apply an information theoretic approach to emoji use for a while, and this seemed like the perfect opportunity. Information theory is the study of storing, transmitting and, most importantly for this project, quantifying information. In other words, using an information theoretic approach we can actually look at two input texts and figure out which one has more information in it. And that’s just what we’re going to do: we’re going to use a measure called “entropy” to directly compare the amount of information in text and emoji.

What’s entropy?

Shannon entropy is a measure of how much information there is in a sequence. Higher entropy means that there’s more uncertainty about what comes next, while lower entropy means there’s less uncertainty.  (Mathematically, entropy is always less than or the same as log2(n), where n is the total number of unique characters. You can learn more about calculating entropy and play around with an interactive calculator here if you’re curious.)

So if you have a string of text that’s just one character repeated over and over (like this: 💀💀💀💀💀) you don’t need a lot of extra information to know what the next character will be: it will always be the same thing. So the string “💀💀💀💀💀” has a very low entropy. In this case it’s actually 0, which means that if you’re going through the string and predicting what comes next, you’re always going to be able to guess what comes next becuase it’s always the same thing. On the other hand, if you have a string that’s made up of four different characters, all of which are equally probable (like this:♢♡♧♤♡♧♤♢), then you’ll have an entropy of 2.

TL;DR: The higher the entropy of a string the more information is in it.

Experiment

Hypothesis

We do have some theoretical maximums for the entropy text and emoji. For text, if the text string is just randomly drawn from the 128 ASCII characters (which isn’t how language works, but this is just an approximation) our entropy would be 7. On the other hand, for emoji, if people are just randomly using any emoji they like from the set of emoji as of June 2017, then we’d expect to see an entropy of around 11.

So if people are just  using letters or emoji randomly, then text should have lower entropy than emoji. However, I don’t think that’s what’s happening. My hypothesis, based on the amount of repetition in emoji, was that emoji should have lower entropy, i.e. less information, than text.

Data

To get emoji and text spans for our experiment I used four different datasets: three from Twitter and one from YouTube.

I used multiple datasets for a couple reasons. First, becuase I wanted a really large dataset of tweets with emoji, and since only between 0.9% and 0.5% of tweets from each Twitter dataset actually contained emoji I needed to case a wide net. And, second, because I’m growing increasingly concerned about genre effects in NLP research. (Like, a lot of our research is on Twitter data. Which is fine, but I’m worried that we’re narrowing the potential applications of our research becuase of it.) It’s the second reason that led me to include YouTube data. I used Twitter data for my initial exploration and then used the YouTube data to validate my findings.

For each dataset, I grabbed all adjacent emoji from a tweet and stored them separately. So this tweet:

Love going to ballgames! ⚾🌭 Going home to work in my garden now, tho 🌸🌸🌸🌸

Has two spans in it:

Span 1:  ⚾🌭

Span 2: 🌸🌸🌸🌸

All told, I ended up with 13,825 tweets with emoji and 18,717 emoji spans of which only 4,713 were longer than one emoji. (I ignored all the emoji spans of length one, since they’ll always have an entropy of 0 and aren’t that interesting to me.) For the YouTube comments, I ended up with 88,629 comments with emoji, 115,707 emoji spans and 47,138 spans with a length greater than one.

In order to look at text as parallel as possible to my emoji spans, I grabbed tweets & YouTube comments without emoji. For each genre, I took a number of texts equal to the number of spans of length > 1 and then calculated the character-level entropy for the emoji spans and the texts.

 

Analysis

First, let’s look at Tweets. Here’s the density (it’s like a smooth histogram, where the area under the curve is always equal to 1 for each group) of the entropy of an equivalent number of emoji spans and tweets.

download (6)
Text has a much high character-level entropy than emoji. For text, the mean and median entropy are both around 5. For emoji, there is a multimodal distribution, with the median entropy being 0 and also clusters around 1 and 1.5.

It looks like my hypothesis was right! At least in tweets, text has much more information than emoji. In fact, the most common entropy for an emoji span is 0: which means that most emoji spans with a length greater than one are just repititons of the same emoji over and over again.

But is this just true on Twitter, or does it extend to YouTube comments as well?

download (5)
The pattern for emoji & text in YouTube comments is very similar to that for Tweets. The biggest difference is that it looks like there’s less information in YouTube Comments that are text-based; they have a mean and median entropy closer to 4 than 5.

The YouTube data, which we have almost ten times more of, corroborates the earlier finding: emoji spans are less informative, and more repetitive, than text.

Which emoji were repeated the most/least often?

Just in case you were wondering, the emoji most likely to be repeated was the skull emoji, 💀. It’s generally used to convey strong negative emotion, especially embarrassment, awkwardness or speechlessness, similar to “ded“.

The least likely was the right-pointing arrow (▶️), which is usually used in front of links to videos.

More info & further work

If you’re interested, the code for my analysis is available here. I also did some of this work as live coding, which you can follow along with on YouTube here.

For future work, I’m planning on looking at which kinds of emoji are more likely to be repeated. My intuition is that gestural emoji (so anything with a hand or face) are more likely to be repeated than other types of emoji–which would definitely add some fuel to the “are emoji words or gestures” debate!

Datasets for data cleaning practice

Looking for datasets to practice data cleaning or preprocessing on? Look no further!

Each of these datasets needs a little bit of TLC before it’s ready for different analysis techniques. For each dataset, I’ve included a link to where you can access it, a brief description of what’s in it, and an “issues” section describing what needs to be done or fixed in order for it to fit easily into a data analysis pipeline.

Big thanks to everyone in this Twitter thread who helped me out by pointing me towards these datasets and letting me know what sort of pre-processing each needed. There were also some other data sources I didn’t include here, so check it out if you need more practice data. And feel free to comment with links to other datasets that would make good data cleaning practice! 🙂

List of datasets:

  • Hourly Weather Surface – Brazil (Southeast region)
  • PhyloTree Data
  • International Comprehensive Ocean-Atmosphere Data Set
  • CLEANEVAL: Development dataset
  • London Air
  • SO MUCH CANDY DATA, SERIOUSLY
  • Production and Perception of Linguistic Voice Quality
  • Australian Marriage Law Postal Survey, 2017
  • The Metropolitan Museum of Art Open Access
  • National Drug Code Directory
  • Flourish OA
  • WikiPlots
  • Register of UK Parliament Members’ Financial Interests
  • NYC Gifted & Talented Scores

Hourly Weather Surface – Brazil (Southeast region)

It’s covers hourly weather data from 122 weathers stations of southeast region (Brazil). The southeast include the states of Rio de Janeiro, São Paulo, Minas Gerais e Espirito Santo. Dataset Source: INMET (National Meteorological Institute – Brazil).

Issues: Can you predict the amount of rain? Temperature? NOTE: Not all weather stations started operating since 2000

PhyloTree Data

Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Human mitochondrial DNA is widely used as tool in many fields including evolutionary anthropology and population history, medical genetics, genetic genealogy, and forensic science. Many applications require detailed knowledge about the phylogenetic relationship of mtDNA variants. Although the phylogenetic resolution of global human mtDNA diversity has greatly improved as a result of increasing sequencing efforts of complete mtDNA genomes, an updated overall mtDNA tree is currently not available. In order to facilitate a better use of known mtDNA variation, we have constructed an updated comprehensive phylogeny of global human mtDNA variation, based on both coding‐ and control region mutations. This complete mtDNA tree includes previously published as well as newly identified haplogroups.

Issues: This data would be more useful if it were in the Newick tree format and could be read in using the read.newick() function. Can you help get the data in this format?

International Comprehensive Ocean-Atmosphere Data Set

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) offers surface marine data spanning the past three centuries, and simple gridded monthly summary products for 2° latitude x 2° longitude boxes back to 1800 (and 1°x1° boxes since 1960)—these data and products are freely distributed worldwide. As it contains observations from many different observing systems encompassing the evolution of measurement technology over hundreds of years, ICOADS is probably the most complete and heterogeneous collection of surface marine data in existence.

Issues: The ICOADS contains O(500M) meteorological observations from ~1650 onwards. Issues include bad observation values, mis-positioned data, missing date/time information, supplemental data in a variety of formats, duplicates etc.

CLEANEVAL: Development dataset

CLEANEVAL is a shared task and competitive evaluation on the topic of cleaning arbitrary web pages, with the goal of preparing web data for use as a corpus, for linguistic and language technology research and development. There are three versions of each file: original, pre-processed, and manually cleaned. All files of each kind are gathered in a directory. The file number remains the same for the three versions of the same file.

Issues: Your task is to “clean up” a set of webpages so that their contents can be easily used for further linguistic processing and analysis. In short, this implies:

  • removing all HTML/Javascript code and “boilerplate” (headers, copyright notices, link lists, materials repeated across most pages of a site, etc.);
  • adding a basic encoding of the structure of the page using a minimal set of symbols to mark the beginning of headers, paragraphs and list elements.

London Air

The London Air Quality Network (LAQN) is run by the Environmental Research Group of King’s College London. LAQN stands for the London Air Quality Network which was formed in 1993 to coordinate and improve air pollution monitoring in London. The network collects air pollution data from London boroughs, with each one funding monitoring in its own area. Increasingly, this information is being supplemented with measurements from local authorities surrounding London in Essex, Kent and Surrey, thereby providing an overall perspective of air pollution in South East England, as well as a greater understanding of pollution in London itself.

Issues: Lots of gaps (null/zero handling), outliers, date handling, pivots and time aggregation needed first!

SO MUCH CANDY DATA, SERIOUSLY

Candy hierarchy data for 2017 Boing Boing Halloween candy hierarchy. This is survey data from this survey.

Issues: If you want to look for longitudinal effects, you also have access to previous datasets. Unfortunate quirks in the data include the fact that the 2014 data is not the raw set (can’t seem to find it), and in 2015, the candy preference was queried without the MEH option.

Production and Perception of Linguistic Voice Quality

Data from the “Production and Perception of Linguistic Voice Quality” project at UCLA. This project was funded by NSF grant BCS-0720304 to Prof. Pat Keating, with Prof. Abeer Alwan, Prof. Jody Kreiman of UCLA, and Prof. Christina Esposito of Macalester College, for 2007-2012.

The data includes spreadsheet files with measures gathered using Voicesauce (Shue, Keating, Vicenik & Yu 2011) for both acoustic measures and EGG measures. The accompanying readme file provides information on the various coding used in both spreadsheets.

Issues: The following issues are with the acoustics measures spreadsheet specifically.

  1. xlsx format with meaningful color coding created by a VBA script (which is copy-pasted into the second sheet)
  2. partially wide format instead of long/tidy, with a ton of columns split into different timepoints
  3. line 6461 has another set of column headers rather than data for some of the columns starting with “shrF0_mean”. I think this was a copy-paste error. Hopefully it doesn’t mean that all of the data below that row is shifted down by 1!

Australian Marriage Law Postal Survey, 2017

Response: Should the law be changed to allow same-sex couples to marry?

Of the eligible Australians who expressed a view on this question, the majority indicated that the law should be changed to allow same-sex couples to marry, with 7,817,247 (61.6%) responding Yes and 4,873,987 (38.4%) responding No. Nearly 8 out of 10 eligible Australians (79.5%) expressed their view.

All states and territories recorded a majority Yes response. 133 of the 150 Federal Electoral Divisions recorded a majority Yes response, and 17 of the 150 Federal Electoral Divisions recorded a majority No response.

Issues: Miles McBain discusses his approach to cleaning this dataset in depth in this blog post.

The Metropolitan Museum of Art Open Access

The Metropolitan Museum of Art provides select datasets of information on more than 420,000 artworks in its Collection for unrestricted commercial and noncommercial use. To the extent possible under law, The Metropolitan Museum of Art has waived all copyright and related or neighboring rights to this dataset using Creative Commons Zero. This work is published from: The United States Of America. You can also find the text of the CC Zero deed in the file LICENSE in this repository. These select datasets are now available for use in any media without permission or fee; they also include identifying data for artworks under copyright. The datasets support the search, use, and interaction with the Museum’s collection.

Issues: Missing values, inconsistent information, missing documentation, possible duplication, mixed text and numeric data.

National Drug Code Directory

The Drug Listing Act of 1972 requires registered drug establishments to provide the Food and Drug Administration (FDA) with a current list of all drugs manufactured, prepared, propagated, compounded, or processed by it for commercial distribution. (See Section 510 of the Federal Food, Drug, and Cosmetic Act (Act) (21 U.S.C. § 360)). Drug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs. FDA publishes the listed NDC numbers and the information submitted as part of the listing information in the NDC Directory which is updated daily.

The information submitted as part of the listing process, the NDC number, and the NDC Directory are used in the implementation and enforcement of the Act.

Issue: Non-trivial duplication (which drugs are different names for the same things?).

Flourish OA

Our data comes from a variety of sources, including researchers, web scraping, and the publishers themselves. All data is cleaned and reviewed to ensure its validity and integrity. Our catalog expands regularly, as does the number of features our data contains. We strive to maintain the most complete and sophisticated store of Open Access data in the world, and it is this mission that drives our continued work and expansion.

A dataset on journal/publisher information that is a bit dirty and might make for great practice. It’s been a graduate student/community project: http://flourishoa.org/

Issues: Scraped data, has some missing fields, possible duplication and some encoding issues (possibly multiple character encodings).

WikiPlots

 

The WikiPlots corpus is a collection of 112,936 story plots extracted from English language Wikipedia. These stories are extracted from any English language article that contains a sub-header that contains the word “plot” (e.g., “Plot”, “Plot Summary”, etc.).

This repository contains code and instructions for how to recreate the WikiPlots corpus.

The dataset itself can be downloaded from here: plots.zip (updated: 09/26/2017). The zip file contains two files:

  • plots: a text file containing all story plots. Each story plot is given with one sentence per line. Each story is followed by on a line by itself.
  • titles: a text file containing a list of titles for each article in which a story plot was found and extracted.

Issues: Some lines may be cut off due to abbreviations. Some plots may be incomplete or contain surrounding irrelevant information.

Register of UK Parliament Members’ Financial Interests

The main purpose of the Register is to provide information about any financial interest which a Member has, or any benefit which he or she receives, which others might reasonably consider to influence his or her actions or words as a Member of Parliament.

Members must register any change to their registrable interests within 28 days. The rules are set out in detail in the Guide to the Rules relating to the Conduct of Members, as approved by the House on 17 March 2015. Interests which arose before 7 May 2015 are registered in accordance with earlier rules.

The Register is maintained by the Parliamentary for Commissioner for Standards. It is updated fortnightly online when the House is sitting, and less frequently at other times. Interests remain on the Register for twelve months after they have expired.

Issues: Each member’s transactions are on a separate webpage with a different text format, with contributions listed under different headings (not necessarily one per line) and in different formats. Will take quite a bit of careful preprocessing to get into CSV or JSON format.

NYC Gifted & Talented Scores

Couple of messy but easy data sets: NYC parents reporting their kids’ scores on the gifted and talented exam, as well as school priority ranking. Some enter the percentiles as point scores, some skip all together, no standard preference format, etc. Also birth quarter affects percentiles.

Analyzing Multilingual Data

This blog post is a little different from my usual stuff. It’s based on a talk I gave yesterday at the first annual Data Institute Conference. As a result, it’s aimed at a slightly more technical audience than my usual stuff, but I hope I’ve done an ok job keeping it accessible. Feel free to drop me a comment if you have any questions or found anything confusing and I’ll be sure to help you out.
You can play with the code yourself by forking this notebook on Kaggle (you don’t even have to download or install anything :).

There are over 7000 languages in the world, 80% of which have fewer than a million speakers each. In fact, six in ten people on Earth speak a language with less than ten million speakers. In other words: the majority of people on Earth use low-resource languages.

As a result, any large sample of user-generated text is almost guaranteed to have multiple languages in it. So what can you do about it? There are a couple options:

  1. Ignore it
  2. Only look at the parts of the data that are in English
  3. Break the data apart by language & use language-specific tools when available

Let’s take a quick look at the benefits and drawbacks of each approach.


Getting started

In [1]:
# import libraries we'll use
import spacy # fast NLP
import pandas as pd # dataframes
import langid # language identification (i.e. what language is this?)
from nltk.classify.textcat import TextCat # language identification from NLTK
from matplotlib.pyplot import plot # not as good as ggplot in R :p

To explore working with multilingual data, let’s look a real-life dataset of user-generated text. This dataset contains 10,502 tweets, randomly sampled from all publicly available geotagged Twitter messages. It’s a realistic cross-section of the type of linguistic diversity you’ll see in a large text dataset.

# read in our data
tweetsData = pd.read_csv("../input/all_annotated.tsv", sep = "\t")

# check out some of our tweets
tweetsData['Tweet'][0:5]
0                            Bugün bulusmami lazimdiii
1       Volkan konak adami tribe sokar yemin ederim :D
2                                                  Bed
3    I felt my first flash of violence at some fool...
4              Ladies drink and get in free till 10:30
Name: Tweet, dtype: object

Option 1: Ignore the multilingualism

Maybe you’ve got a deadline coming up fast, or maybe you didn’t get a chance to actually look at some of your text data and just decide to treat it as if it were English. What could go wrong?

To find out, let’s use Spacy to tokenize all our tweets and take a look at the longest tokens in our data.

Spacy is an open-source NLP library that is much faster than the Natural Language Toolkit, although it does not have as many tasks implemented. You can find more information in the Spacy documentation.

# create a Spacy document of our tweets
# load an English-language Spacy model
nlp = spacy.load("en")

# apply the english language model to our tweets
doc = nlp(' '.join(tweetsData['Tweet']))

Now let’s look at the longest tokens in our Twitter data.

sorted(doc, key=len, reverse=True)[0:5]
[a7e78d48888a6811d84e0759e9387647447d1e74d8c7c4f1bec00d318e4e5030f08eb35668a97873820ca1d9dc61ffb620f8992296f3b029a60f153beac8018f5fb77d000000,
 e44337d70d7a7fec79a8b6bd8aa573367224023e4272f22af6d0844d9682d5b48062e331b33ab3b92dac2c262ed4f154ba679ad07b30d2cf1c15851cdac901315b4e72000000,
 3064d36c909f9d437f7a3f405aa550f65529566547ae2308d6c4f2585250106d33b924ae9c8dcc08856e41f611d9bd15409a79f7ba21d318ab484f0cae10017201590a000000,
 69bdf5177f1ae8ed61ed71c477f7dc415b97a2b2d7e57be079feb1a2c52600a996fd0891e130c1ce13c94e4406f83ba59e5edb5a7e0fb45e5251a17bb29601081f3de0000000,
 lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3&lt;3]

The five longest tokens are entire tweets, four produced by an art bot that tweets hashes of Unix timestamps and one that’s just the HTML version of “<3” tweeted a bunch of times. In other words: normal Twitter weirdness. This is actual noise in the data and can be safely discarded without hurting downstream tasks, like sentiment analysis or topic modeling.

sorted(doc, key=len, reverse=True)[6:10]
[卒業したった(*^^*)\n彼女にクラスで一緒にいるやつに\nたった一人の同中の拓夢とも写真撮れたし満足や!(^。^)時間ギリギリまでテニスやってたからテニス部面と写真撮ってねーわ‼︎まぁこいつらわこれからも付き合いあるだろうからいいか!,
 眼鏡は近視用で黒のセルフレームかアンダーリムでお願いします。オフの日は赤いセルフレームです。形状はサークルでお願いします。30代前半です。髪型ボブカットもしくはティモシェンコ元ウクライナ首相みたいなので。色は黒目でとりあえずお願いします,
 普段は写真撮られるの苦手なので、\n\n顔も出さずw\n\n登場回数少ないですが、\n\n元気にampで働いておりますw\n\n一応こんな人が更新してますのでw\n\n#takahiromiyashitathesolois,
 love#instagood#me#cute#tbt#photooftheday#instamood#tweegram#iphonesia#picoftheday#igers#summer#girl#insta]

The next five longest tokens are also whole tweets which have been identified as single tokens. In this case, though, they were produced by humans!

The tokenizer (which assumes it will be given mainly English data) fails to correct tokenize these tweets because it’s looking for spaces. These tweets are in Japanese, though, and like many Asian languages (including all varieties of Chinese, Korean and Thai) they don’t actually use spaces between words.

In case you’re curious, “、” and “。” are single characters and don’t contain spaces! They are, respectively, the ideographic comma and ideographic full stop, and are part of a very long list of line breaking characters associated with specific orthographic systems.

In order to correctly tokenize Japanese, you’ll need to use a language-specific tokenizer.

The takeaway: if you ignore multiple languages, you’ll end up violating the assumptions behind major out-of-the-box NLP tools


Option 2: Only look at the parts of the data that are in English

So we know that just applying NLP tools designed for English willy-nilly won’t work on multiple languages. So what if we only grabbed the English-language data and then worked with that?

There are two big issues here:

  • Correctly identifying which tweets are in English
  • Throwing away data

Correctly identifying which tweets are in English

Probably the least time-intensive way to do this is by attempting to automatically identify the language that each Tweet is written in. A BIG grain of salt here: automatic language identifiers are very error prone, especially on very short texts. Let’s check out two of them.

  • LangID: Lui, Marco and Timothy Baldwin (2011) Cross-domain Feature Selection for Language Identification, In Proceedings of the Fifth International Joint Conference on Natural Language Processing (IJCNLP 2011), Chiang Mai, Thailand, pp. 553—561. Available from http://www.aclweb.org/anthology/I11-1062
  • TextCat: Cavnar, W. B. and J. M. Trenkle, “N-Gram-Based Text Categorization” In Proceedings of Third Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, UNLV Publications/Reprographics, pp. 161-175, 11-13 April 1994.

First off, here are the languages the first five tweets are actually written in, hand tagged by a linguist (i.e. me):

  1. Turkish
  2. Turkish
  3. English
  4. English
  5. English

Now let’s see how well two popular language identifiers can detect this.

# summerize the labelled language
tweetsData['Tweet'][0:5].apply(langid.classify)
0     (az, -30.30187177658081)
1     (ms, -83.29260611534119)
2      (en, 9.061840057373047)
3    (en, -195.55468368530273)
4     (en, -98.53013229370117)
Name: Tweet, dtype: object

LangID does…alright, with three out of five tweets identified correctly. While it’s pretty good at identifying English, the first tweet was identified as Azerbaijani and the second tweet was labeled as Malay, which is very wrong (not even in the same language family as Turkish).

Let’s look at another algorithm, TextCat, which is based on character-level N-Grams.

# N-Gram-Based Text Categorization
tc = TextCat()

# try to identify the languages of the first five tweets again
tweetsData['Tweet'][0:5].apply(tc.guess_language)
0    tur
1    ind
2    bre
3    eng
4    eng
Name: Tweet, dtype: object

TextCat also only got three out of the five correct. Oddly, it identifier “bed” as Breton. To be fair, “bed” is the Breton word for “world”, but it’s still a bit odd.

The takeaway: Automatic language identification, especially on very short texts, is very error prone. (I’d recommend using multiple language identifiers & taking the majority vote.)

Throwing away data

Even if language identification were very accurate, how much data would be just be throwing away if we only looked at data we were fairly sure was English?

Note: I’m only going to LangID here for time reasons, but given the high error rate I’d recommend using multiple language identification algorithms.

# get the language id for each text
ids_langid = tweetsData['Tweet'].apply(langid.classify)

# get just the language label
langs = ids_langid.apply(lambda tuple: tuple[0])

# how many unique language labels were applied?
print("Number of tagged languages (estimated):")
print(len(langs.unique()))

# percent of the total dataset in English
print("Percent of data in English (estimated):")
print((sum(langs=="en")/len(langs))*100)
Number of tagged languages (estimated):
95
Percent of data in English (estimated):
40.963625976

Only 40% of our data has been tagged as English by LangId. If we throw the rest of it, we’re going to lose more than half of our dataset! Especially if this is data you spent a lot of time and money collecting, that seems downright wasteful. (Plus, it might skew our analysis.)

So if 40% of our data is in English, what is the other 60% made up of? Let’s check out the distribution data across languages in our dataset.

# convert our list of languages to a dataframe
langs_df = pd.DataFrame(langs)

# count the number of times we see each language
langs_count = langs_df.Tweet.value_counts()

# horrible-looking barplot (I would suggest using R for visualization)
langs_count.plot.bar(figsize=(20,10), fontsize=20)

There’s a really long tail on our dataset; most that were identified in our dataset were only identified a few times. This means that we can get a lot of mileage out of including just a few more popular languages in our analysis. How much will we gain, exactly?

print("Languages with more than 400 tweets in our dataset:")
print(langs_count[langs_count > 400])

print("")

print("Percent of our dataset in these languages:")
print((sum(langs_count[langs_count > 400])/len(langs)) * 100)
Languages with more than 400 tweets in our dataset:
en    4302
es    1020
pt     751
ja     436
tr     414
id     407
Name: Tweet, dtype: int64

Percent of our dataset in these languages:
69.7962292897

By including only five more languages in our analysis (Spanish, Portugese, Japanese, Turkish and Indonesian) we can increase our coverage of the data in our dataset by almost a third!

The takeaway: Just incorporating a couple more languages in your analysis can give you access to a lot more data!


Option 3: Break the data apart by language & use language-specific tools

Ok, so what exactly does this pipeline look like? Let’s look at just the second most popular language in our dataset: Spanish. What happens when we pull out just the Spanish tweets & tokenize them?

# get a list of tweets labelled "es" by langid
spanish_tweets = tweetsData['Tweet'][langs == "es"]

# load a Spanish-language Spacy model
from spacy.es import Spanish
nlp_es = Spanish(path=None)

# apply the Spanish language model to our tweets
doc_es = nlp_es(' '.join(spanish_tweets))

# print the longest tokens
sorted(doc_es, key=len, reverse=True)[0:5]
[ViernesSantoEnElColiseoRobertoClemente,
 MiFantasia1DEnWembleyConCocaColaFM,
 fortaleciéndonos','escenarios,
 DirectionersConCocaColaFM1D,
 http://t.co/ezZEsXN3MF\nvia]

This time, the longest tokens are Spanish-language hashtags. This is exactly the sort of thing we’d expect to see! From here, we can use this tokenized dataset to feed into other downstream like sentiment analysis.

Of course, it would be impractical to do this for every single language in our dataset, even if we could be sure that they were all identified correctly. You’re probably going to have to accept that you probably won’t be able to consider every language in your dataset unless you can commit a lot of time. But including any additional language will enrich your analysis!

The takeaway: It doesn’t have to be onerous to incorporate multiple languages in your analysis pipeline!


So let’s review our options for analyzing multilingual data:

Option 1: Ignore Multilingualism

As we saw, this option will result in violating a lot of the assumptions built into NLP tools (e.g. there are spaces between words). If you do this, you’ll end up with a lot of noise and headaches as you try to move through your analysis pipeline.

Option 2: Only look at English

In this dataset, only looking at English would have led to us throwing away over half of our data. Especailly as NLP tools are developed and made avaliable for more and more languages, there’s less reason to stick to English-only NLP.

Option 3: Seperate your data by language & analyze them independently

This does take a little more work than the other options… but not that much more, especially for languages that already have resources avalialbe for them.

Additional resources:

Language Identification:

Here are some pre-built language identifiers to use in addition to LandID and TextCat:

Dealing with texts which contain multiple languages (code switching):

It’s very common for a span of text to include multiple languages. This example contains English and Malay (“kain kain” is Malay for “unwrap”):

Roasted Chicken Rice with Egg. Kain kain! 🙂 [Image of a lunch wrapped in paper being unwrapped.]

How to automatically handle code switching is an active research question in NLP. Here are some resources to get you started learning more:

 

Where can you find language data on the web?

In the course of my day-to-day work on Kaggle’s public data platform, I’ve learned a lot about the ecosystem of language data on the web (or at least the portions of it that have been annotated in English). For example, I’ve noticed a weird disconnect between European and American data repositories  resources that I’m pretty sure has its roots in historical and disciplinary divisions.

Computer Used to Create Printouts of Data (FDA 097) (8250815324)

I’ve also found a lot of great resources, though! At some point, I started keeping notes on interesting data repositories and link aggregators. I finally got around to tidying up and annotating my list of resources, and I figured that it would a useful thing to share with everyone. So, without further ado, here’s an (incomplete) list of some places to find language resources on the web:

  • META-SHARE
    • URL :http://www.meta-share.org/
    • META-SHARE has a lot of resources from The International Conference on Language Resources and Evaluation (LREC) on it.
  • Trolling
  • Linguistic Data Consortium (LDC)
    • URL: https://www.ldc.upenn.edu/
    • The Linguistic Data Consortium is an international non-profit that offers archival hosting of datasets. The data offered by them is high quality and usually not free (although they offer data grants for students).
  • Kaggle
    • URL: https://www.kaggle.com/datasets?search=corpus
    • Kaggle’s public data platform has a lot of language/NLP datasets available on it, many not in English. You can also do data analysis on Kaggle (with R or Python) without having to download anything or set up a local environment.
  • European Language Resources Association
  • Zenodo
    • URL: https://zenodo.org/
    • Hosted by CERN, has datasets (including corpora) from a wide variety of disciplines.
  • Document the Now
    • URL: http://www.docnow.io/catalog/
    • Contains lists of Tweet ID’s surrounding certain events. You’ll need to use the “rehydrator” to get the actual tweets.
  • International Standard Language Resource Number
    • URL: http://www.islrn.org/resources/identify_name/  (a list of unique ID #’s associated with language resources)
    • Like a digital object identifier (DOI) for language resources. Not the best search (only looks at the title)  but if you have a specific phrase you’re looking for it can be a good way to discover new resources.
  • Language & Culture Archives (SIL)
  • Open Language Archives Community (OLAC)
  • Free sound
  • GitHub
    • URL:  https://github.com/search?q=corpus
    • You can sometimes find interesting & high quality language data on Github, but it’s not centralized and of widely varying quality.
  • Re3data.org
  • Language Gold Mine

Know of a resource I forgot to include? Link it in the comments!