My job at Twitter
is to ensure user trust,
protect user rights and keep users safe,
both from each other
and, at times, from themselves.
Let's talk about what scale looks like at Twitter.
Back in January 2009,
we saw more than two million new tweets each day
on the platform.
January 2014, more than 500 million.
We were seeing two million tweets
in less than six minutes.
That's a 24,900-percent increase.
Now, the vast majority of activity on Twitter
puts no one in harm's way.
There's no risk involved.
My job is to root out and prevent activity that might.
Sounds straightforward, right?
You might even think it'd be easy,
given that I just said the vast majority
of activity on Twitter puts no one in harm's way.
Why spend so much time
searching for potential calamities
in innocuous activities?
Given the scale that Twitter is at,
a one-in-a-million chance happens
500 times a day.
It's the same for other companies
dealing at this sort of scale.
For us, edge cases,
those rare situations that are unlikely to occur,
are more like norms.
Say 99.999 percent of tweets
pose no risk to anyone.
There's no threat involved.
Maybe people are documenting travel landmarks
like Australia's Heart Reef,
or tweeting about a concert they're attending,
or sharing pictures of cute baby animals.
After you take out that 99.999 percent,
that tiny percentage of tweets remaining
works out to roughly
150,000 per month.
The sheer scale of what we're dealing with
makes for a challenge.
You know what else makes my role
People do weird things.
And I have to figure out what they're doing,
why, and whether or not there's risk involved,
often without much in terms of context
I'm going to show you some examples
that I've run into during my time at Twitter --
these are all real examples —
of situations that at first seemed cut and dried,
but the truth of the matter was something
The details have been changed
to protect the innocent
and sometimes the guilty.
We'll start off easy.
If you saw a Tweet that only said this,
you might think to yourself,
"That looks like abuse."
After all, why would you
want to receive the message,
Now, I try to stay relatively hip
to the latest trends and memes,
so I knew that "yo, bitch"
was also often a common greeting between friends,
as well as being a popular "Breaking Bad" reference.
I will admit that I did not expect
to encounter a fourth use case.
It turns out it is also used on Twitter
when people are role-playing as dogs.
And in fact, in that case,
it's not only not abusive,
it's technically just an accurate greeting.
So okay, determining whether or not
something is abusive without context,
Let's look at spam.
Here's an example of an account engaged
in classic spammer behavior,
sending the exact same message
to thousands of people.
While this is a mockup I put
together using my account,
we see accounts doing this all the time.
Seems pretty straightforward.
We should just automatically suspend accounts
engaging in this kind of behavior.
Turns out there's some exceptions to that rule.
Turns out that that message
could also be a notification
you signed up for that the International
Space Station is passing overhead
because you wanted to go outside
and see if you could see it.
You're not going to get that chance
if we mistakenly suspend the account
thinking it's spam.
Okay. Let's make the stakes higher.
Back to my account,
again exhibiting classic behavior.
This time it's sending the same message and link.
This is often indicative of
something called phishing,
somebody trying to steal another
person's account information
by directing them to another website.
That's pretty clearly not a good thing.
We want to, and do, suspend accounts
engaging in that kind of behavior.
So why are the stakes higher for this?
Well, this could also be a bystander at a rally
who managed to record a video
of a police officer beating a non-violent protester
who's trying to let the world know what's happening.
We don't want to gamble
on potentially silencing that crucial speech
by classifying it as spam and suspending it.
That means we evaluate hundreds of parameters
when looking at account behaviors,
and even then, we can still get it wrong
and have to reevaluate.
Now, given the sorts of challenges I'm up against,
it's crucial that I not only predict
but also design protections for the unexpected.
And that's not just an issue for me,
or for Twitter, it's an issue for you.
It's an issue for anybody who's building or creating
something that you think is going to be amazing
and will let people do awesome things.
So what do I do?
I pause and I think,
how could all of this
go horribly wrong?
I visualize catastrophe.
And that's hard. There's a sort of
inherent cognitive dissonance in doing that,
like when you're writing your wedding vows
at the same time as your prenuptial agreement.
But you still have to do it,
particularly if you're marrying
500 million tweets per day.
What do I mean by "visualize catastrophe?"
I try to think of how something as
benign and innocuous as a picture of a cat
could lead to death,
and what to do to prevent that.
Which happens to be my next example.
This is my cat, Eli.
We wanted to give users the ability
to add photos to their tweets.
A picture is worth a thousand words.
You only get 140 characters.
You add a photo to your tweet,
look at how much more content you've got now.
There's all sorts of great things you can do
by adding a photo to a tweet.
My job isn't to think of those.
It's to think of what could go wrong.
How could this picture
lead to my death?
Well, here's one possibility.
There's more in that picture than just a cat.
When you take a picture with your smartphone
or digital camera,
there's a lot of additional information
saved along in that image.
In fact, this image also contains
the equivalent of this,
more specifically, this.
Sure, it's not likely that someone's going to try
to track me down and do me harm
based upon image data associated
with a picture I took of my cat,
but I start by assuming the worst will happen.
That's why, when we launched photos on Twitter,
we made the decision to strip that geodata out.
If I start by assuming the worst
and work backwards,
I can make sure that the protections we build
work for both expected
and unexpected use cases.
Given that I spend my days and nights
imagining the worst that could happen,
it wouldn't be surprising if
my worldview was gloomy.
The vast majority of interactions I see --
and I see a lot, believe me -- are positive,
people reaching out to help
or to connect or share information with each other.
It's just that for those of us dealing with scale,
for those of us tasked with keeping people safe,
we have to assume the worst will happen,
because for us, a one-in-a-million chance
is pretty good odds.