The curly fry conundrum: Why social media “likes” say more than you might think

If you remember that first decade of the web,

it was really a static place.

You could go online, you could look at pages,

and they were put up either by organizations

who had teams to do it

or by individuals who were really tech-savvy

for the time.

And with the rise of social media

and social networks in the early 2000s,

the web was completely changed

to a place where now the vast majority of content

we interact with is put up by average users,

either in YouTube videos or blog posts

or product reviews or social media postings.

And it's also become a much more interactive place,

where people are interacting with others,

they're commenting, they're sharing,

they're not just reading.

So Facebook is not the only place you can do this,

but it's the biggest

and it serves to illustrate the numbers.

Facebook has 1.2 billion users per month.

So half the Earth's internet population

is using Facebook.

They are a site, along with others,

that has allowed people to create an online persona

with very little technical skill,

and people responded by putting huge amounts

of personal data online.

So the result is that we have behavioral,

preference, demographic data

for hundreds of millions of people,

which is unprecedented in history.

And as a computer scientist, what this means is that

I've been able to build models

that can predict all sorts of hidden attributes

for all of you that you don't even know

you're sharing information about.

As scientists, we use that to help

the way people interact online,

but there's less altruistic applications,

and there's a problem in that users don't really

understand these techniques and how they work,

and even if they did, they don't
have a lot of control over it.

So what I want to talk to you about today

is some of these things that we're able to do,

and then give us some ideas
of how we might go forward

to move some control back into the hands of users.

So this is Target, the company.

I didn't just put that logo

on this poor, pregnant woman's belly.

You may have seen this anecdote that was printed

in Forbes Magazine where Target

sent a flyer to this 15-year old girl

with advertisements and coupons

for baby bottles and diapers and cribs

two weeks before she told her parents

that she was pregnant.

Yeah, the dad was really upset.

He said, "How did Target figure out

that this high school girl was pregnant

before she told her parents?"

It turns out that they have the purchase history

for hundreds of thousands of customers

and they compute what they call a pregnancy score,

which is not just whether or not a woman's pregnant,

but what her due date is.

And they compute that

not by looking at, like, the obvious things,

like, she's buying a crib or baby clothes,

but things like, she bought more vitamins

than she normally had,

or she bought a handbag

that's big enough to hold diapers.

And by themselves, those purchases don't seem

like they might reveal a lot,

but it's a pattern of behavior that,

when you take it in the context of other people,

starts to actually reveal some insights.

So that's the kind of thing that we do

when we're predicting stuff
about you on social media.

We're looking for little
patterns of behavior that,

when you detect them among millions of people,

let's us find out all kinds of things.

So in my lab and with colleagues,

we've developed mechanisms where we can

quite accurately predict things

like your political preference,

your personality score, gender, sexual orientation,

religion, age, intelligence,

along with things like

how much you trust the people you know

and how strong those relationships are.

We can do all of this really well.

And again, it doesn't come from what you might

think of as obvious information.

So my favorite example is from this study

that was published this year

in the Proceedings of the National Academies.

If you Google this, you'll find it.

It's four pages, easy to read.

And they looked at just people's Facebook likes,

so just the things you like on Facebook,

and used that to predict all these attributes,

along with some other ones.

And in their paper they listed the five likes

that were most indicative of high intelligence.

And among those was liking a page

for curly fries. (Laughter)

Curly fries are delicious,

but liking them does not necessarily mean

that you're smarter than the average person.

So how is it that one of the strongest indicators

of your intelligence

is liking this page

when the content is totally irrelevant

to the attribute that's being predicted?

And it turns out that we have to look at

a whole bunch of underlying theories

to see why we're able to do this.

One of them is a sociological
theory called homophily,

which basically says people are
friends with people like them.

So if you're smart, you tend to
be friends with smart people,

and if you're young, you tend
to be friends with young people,

and this is well-established

for hundreds of years.

We also know a lot

about how information spreads through networks.

It turns out things like viral videos

or Facebook likes or other information

spreads in exactly the same way

that diseases spread through social networks.

So this is something we've studied for a long time.

We have good models of it.

And so you can put those things together

and start seeing why things like this happen.

So if I were to give you a hypothesis,

it would be that a smart guy started this page,

or maybe one of the first people who liked it

would have scored high on that test.

And they liked it, and their friends saw it,

and by homophily, we know that
he probably had smart friends,

and so it spread to them, and some of them liked it,

and they had smart friends,

and so it spread to them,

and so it propagated through the network

to kind of a host of smart people,

so that by the end, the action

of liking the curly fries page

is indicative of high intelligence,

not because of the content,

but because the actual action of liking

reflects back the common attributes

of other people who have done it.

So this is pretty complicated stuff, right?

It's a hard thing to sit down and explain

to an average user, and even if you do,

what can the average user do about it?

How do you know that you've liked something

that indicates a trait for you

that's totally irrelevant to the
content of what you've liked?

There's a lot of power that users don't have

to control how this data is used.

And I see that as a real problem going forward.

So I think there's a couple path

that we want to look at

if we want to give users some control

over how this data is used,

because it's not always going to be used

for their benefit.

An example I often give is that,

if I ever get bored being a professor,

I'm going to go start a company

that predicts all of these attributes

and things like how well you work in teams

and if you're a drug user, if you're an alcoholic.

We know how to predict all that.

And I'm going to sell reports

to h.r. companies and big businesses

that want to hire you.

We totally can do that now.

I could start that business tomorrow,

and you would have absolutely no control

over me using your data like that.

That seems to me to be a problem.

So one of the paths we can do down

is the policy and law path.

And in some respects, I think
that that would be most effective,

but the problem is we'd actually have to do it.

Observing our political process in action

makes me think it's highly unlikely

that we're going to get a bunch of representatives

to sit down, learn about this,

and then enact sweeping changes

to intellectual property law in the U.S.

so users control their data.

We could go the policy route,

where social media companies say,

you know what? You own your data.

You have total control over how it's used.

The problem is that the revenue models

for most social media companies

rely on sharing or exploiting users data in some way.

It's sometimes said of Facebook that the users

aren't the customer, they're the product.

And so how do you get a company

to cede control of their main asset

back to the users?

It's possible, but I don't think it's something

that we're going to see change quickly.

So I think the other path

that we can do down that's
going to be more effective

is one of more science.

It's doing science that allowed us to develop

all these mechanisms for computing

this personal data data in the first place.

And it's actually very similar research

that we'd have to do

if we want to develop mechanisms

that can say to a user,

"Here's the risk of that action you just took."

You know, by liking that Facebook page,

or by sharing this piece of personal information,

you've now improved my ability

to predict whether or not you're using drugs

or whether or not you get
along well in the workplace.

And that, I think, can affect whether or not

people want to share something,

keep it private, or just keep it offline altogether.

We can also look at things like

allowing people to encrypt data that they upload,

so it's kind of invisible and worthless

to sites like Facebook

or third party services that access it,

but that select users who the person who posted it

want to see it have access to see it.

This is all super-exciting research

from an intellectual perspective,

and so scientists are going to be willing to do it.

So that gives us an advantage over the loss side.

One of the problems that people bring up

when I talk about this is, they say,

you know, if people start
keeping all this data private,

all those methods that you've been developing

to predict their traits are going to fail.

And I say, absolutely, and for me, that's success,

because as a scientist,

my goal is not to infer information about users,

it's to improve the way people interact online.

And sometimes that involves
inferring things about them,

but if users don't want me to use that data,

I think they should have the right to do that.

I want users to be informed and consenting

users of the tools that we develop.

And so I think encouraging this kind of science

and supporting researchers

who want to cede some of that control back to users

and away from the social media companies

means that going forward as these tools evolve

and advance

means that we're going to have an educated

and empowered user base,

and I think all of us can agree

that that's a pretty ideal way to go forward.

Thank you.

(Applause)