thank you so much for having me as we said in my bio I'm the engineer at
LinkedIn I work on a design systems team but I would like to think that is the
least interesting thing about me so we're not gonna talk about that today
outside of work I consider myself an artist I try to
make a lot of things ranging from Twitter BOTS to satirical web projects
my process looks something like this which is a lie if I mean honest is what
I would like it to look like what it actually looks like is something like
this with lots of crippling self-doubt and a lot of pivoting in the middle but
something that has been a part of my process for the longest time has been
data since for as long as I remember from my first website that I created my
ideas have always been informed by data and then also data has always been the
idea itself I think many of you may be familiar with the former ideas mean
informed by data and design and development we use data to back up our
ideas or to go against them we use it to measure the impact of a decision but in
those cases the data is still secondary for me data has always been the catalyst
for the idea not just a tool to aid it because there's always a reason for data
to be compiled or collected a dataset is inherently an idea a way of saying there
might be something here so just like big companies collect data about us because
they know it tells a story it might be an incomplete story or a false one but
it's still a story nonetheless one they can sell to advertisers the same way
they do that we can also collect data about ourselves in the world we live in
to answer our own questions so one of my favorite examples of this is call me
Adele by an artist and programmer her name is what we Poway
so she gathered the chat history of the first four months of a long-distance
relationship she was in for four years she gathered the first four months and
then the last four months and after using text analysis on the data she was
able to discover certain things about herself like how towards the last months
of the relationship she became more dependent I was the one sending the
initial text message instead of him or her partner being the
one reaching out another project I really like is everything like
everything I've ever wanted to know by Martin Sims so it's just a drop-down
list of everything she's ever googled
from 2004 to 2007 but lastly I think there's nothing that encapsulates the
idea of data as an idea than the work of Julia Penelope she's a linguist
she's towered the Oxford English Dictionary one by one going line by line
through every word in 1977 to make her own dataset and keep in mind in 1977 the
Oxford English Dictionary was about three hundred thousand words so she was
doing research in two sections in the language specifically she was trying to
find out how many words they were in the English dictionary like referred to a
woman as a prostitute so she found two hundred and twenty and four men she
found twenty-two so just by taking words that fit a certain criteria out of the
dictionary she was able to find a pattern and tell a story of what the
English language had to say about men and women so it was a story I wanted to
keep telling I wanted to be able to explore beyond names for a prostitute
and look at the words that that there were in the English dictionary that were
gendered so either described a man or woman and this is like the binary
definition of gender I'm not gonna go into how there's more but I wanted to go
into gender dictionary basically and that's what this talk is about not about
data-driven design but it's about using code to create your own data set so the
first question to ask okay this is what I get for trying to
look stylish so the first question to ask is where and how can I get this data
I mean it's usually one of these three ways API is static data web scraping so
before I started the process of gathering data I had to know what my
criteria was so what was a gendered word for example this would be a gendered
word because it has man of the definition this would be a gendered word
because it has female in the definition so different for me I'm a gendered word
was the word that had man woman female male in their definition so next step I
started looking for some API s and the first thing I went I did was go to
programmable web comm which is API heaven there are API is for all sorts of
things and if you're not familiar with what an API is it's an interface or
service like you have a wall socket that's an interface to get electricity
and API is an interface to get all sorts of things like this is Google this is a
search for food api's which has a bunch of stuff like recipes um so also like
social media sites create API so we can interact with your data for example I
made this website that uses the Twitter API to tell you whether you're a tweeter
or a retweet err because I wanted to convince my friend that I was like
original but I think I'm actually a retweet her in that oh never mind
anyway when I searched for dictionary api's I found one code Wernick so they
have a reverse dictionary which means find all the words that have this word
in its definition so for my use case an example of a
gendered word is one that has male/female woman man so this is the
reverse dictionary for a woman so all of these words have women in their
definitions so I made a request to the API for the reverse dictionary of women
and all the other terms and I got back for each request I got back a response
that looks like this so this is JSON format and that means
um it's a sequence of data in key and value so you can see highlighted key
would be the word and then Valley would be the matron and then everything else
is still in that same format so unfortunately I found out this happens a
lot with api's because they want you to pay money so I wasn't giving me
everything it says they have about three thousand words
in the API but they only gave me back like four hundred so I had to find
another way to get the data and the second way is static datasets or the
context of this talk the static data set is data that's in a file like CSV JSON
txt for example this project called Wi-Fi I was home in Nigeria and I was
thinking of how slow the internet was so I got a static data set of Wi-Fi speeds
online which was in CSV format it's on the top right which is basically a
spreadsheet and then I used it to create a site that allows you to experience
different ways Wi-Fi speeds as loading screens a popular website so this is how
long it would take you to load Netflix in Egypt I don't know if i cool so I
looked on github and I found a dictionary in JSON format which looked
like this sorry I didn't get to find all the gendered words in here where it's
like these so if we go back to our definition of gendered words remember a
gendered word is one with one of these terms in this definition and we can grab
all those words using something called regular expressions so regular
expression is a sequence of characters that defines a search pattern if we go
back to the Kamiya del project one of the things she tries to measure is how
many times her partner would say her name or a nickname for her during the
first four months and doing the last four months so instead of like looking
for each instance of her name she could just create one regular expression that
covered all of the nicknames so boo thing day babe baby so in this case all
of the words at the bottom or matches for that singular string at the top so
you can do the same thing for these words for a brief explanation the
question mark means everything before me is optional the group is just for
grouping and then the bar in the middle is saying
match either thing on either side so we'll match these or match these so you
can see all the words on the bottom or matches for that singular expression and
so when I use this to filter the data set it came out to be a thousand words
but when I went through the list there were some words that weren't actually
gendered so I had to find a way to get rid of them words like rabbit test so I
decided I would look for patterns in the incorrect words the words that weren't
actually gendered so I can find a way to remove them and one of the patterns I
found was very specific its words that were basically for for people and not
used to describe people for example like there's Peter and there is men let like
mantlet is a cape worn by women it's not a word that describes women I'm and then
Peter is a name form and not a gendered word um so luckily for me there are
libraries like an LT K which stands for natural language processing tool kit
that allows you to process the English language and what that means is if you
give it a sentence it can tell you for example how many nouns are in the
sentence how many verbs whether the sentence is positive or negative so the
first thing you do before you process a sentence is you break it up into pieces
just like chop it apart which is what's happening on the right and then after we
tokenize it which means chop it apart we can use the NLT kay part of speech
tagger well which will tell you for each word in the sentence on what part of
speech is so we can see and man is n N and n n is a noun
baptismal and comment or JJ which means they're adjectives and I know that
because I looked it up so I can check what the word was before
the gender term so I can see that four comes before a man so I can just remove
Peter from the dictionary and say oh it's not a gender the other pattern was
clothing items like words like skivvies which I thought was the name of a
disease but it's actually men's underwear and then pajama and hobble
skirt so I found a list of clothing items so I can move all the words that
are secretly clothing items and pretending to be gendered words
unfortunately the website where I found the list you have to pay to make API
calls and I didn't want to do that so I decided I would scrape your website and
said to get information that I wanted and the web scraping is the third tool
for getting information that you probably shouldn't be getting and its
basic you grabbed its it consists of grabbing the HTML that makes up the
website and then you can look through it and find what you want so the steps are
opening the URL getting the HTML and then finding what you wanted the HTML so
for the last step to find what you wanted the HTML you can look in your
browser's inspector which is just a right click and then inspect so I see
the data that I want is in a link that's the child of a spin with the class TD
and so with that information I'm able to get all the websites that they did not
want me to get without paying and then I use this to filter the word list again
and then it comes down to 4,000 words so the last thing I wanted to do is find
gender opposites so I wanted to match words to the opposite ladies gentlemen
and wife's husband and I can do that using something called words avec words
avec transforms words into vectors you feel the algorithm a ton of sex and then
groups words together based on how they appear sorry
the in groups words together to the together if they appear in this new
context so you can see kitchen is next to that's actually wrong bathtub is next
to faucet well it's not perfect cuz it's code so garden is supposed to host and
then tool is close to drill color is close to paint it's based on the idea
that a word is characterized by the company keeps so if you look at this
conference and you like you can assume I mean you probably shouldn't but you can
assume that somebody is either a designer a developer because Awards
conferences for designers and developers the same way by the context that word is
being used in a sentence you can assume whether it's like another word so in
this case since salt and seasoning here in the same context you can assume
that they're like salt is closer to seasoning than it is to like chair and
with that model we can do stuff like getting the similarity of words and this
is like just an online demo of it I kind of understand on a basic level how this
works and that's kind of all you need to understand I don't really know about
like all that really like hard algorithmic stuff but you don't need to
know that's to actually use it so you can see like the similarity between
women and wife it's closer than the similarity between women and rectangle
and then you can also get weird analogies so you can do a woman is the
queen as man is to King and that's it so I got the initial word set using API s
and my finding static datasets and then I cleaned and filtered the data set with
reject regex web scraping and ltk then I use worked back to find the word
opposites and their equivalents and I gathered all the data and I was able to
create the interface with a friend of mine so included words and their gender
opposites and I really like that you're able to see an explore bias and language
including what words have undergo undergone semantics derogation which is
the process that words take on more negative connotations than they
originally we're to find us so you can see like master and mistress or like
kind of very different words now mistress has taken on a more negative
connotation over the years and also which words or named phenomena is
specific to a gender like there is no female equivalent for sissy so creating
this data set allowed me to have something to point to when I wanted to
claim that language can be sexist it also allowed me to tell story and create
discussion about something that I believed and then lastly I think make
what data making can do for you is it can help you answer questions about
yourself and also about the world we live in
thank you