Del Harvey: The strangeness of scale at Twitter

105,103 views ・ 2014-03-27

TED


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00:12
My job at Twitter
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is to ensure user trust,
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protect user rights and keep users safe,
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both from each other
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and, at times, from themselves.
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Let's talk about what scale looks like at Twitter.
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Back in January 2009,
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we saw more than two million new tweets each day
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on the platform.
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January 2014, more than 500 million.
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We were seeing two million tweets
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in less than six minutes.
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That's a 24,900-percent increase.
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Now, the vast majority of activity on Twitter
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puts no one in harm's way.
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There's no risk involved.
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My job is to root out and prevent activity that might.
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Sounds straightforward, right?
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You might even think it'd be easy,
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given that I just said the vast majority
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of activity on Twitter puts no one in harm's way.
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Why spend so much time
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searching for potential calamities
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in innocuous activities?
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Given the scale that Twitter is at,
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a one-in-a-million chance happens
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500 times a day.
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It's the same for other companies
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dealing at this sort of scale.
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For us, edge cases,
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those rare situations that are unlikely to occur,
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are more like norms.
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Say 99.999 percent of tweets
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pose no risk to anyone.
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There's no threat involved.
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Maybe people are documenting travel landmarks
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like Australia's Heart Reef,
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or tweeting about a concert they're attending,
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or sharing pictures of cute baby animals.
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After you take out that 99.999 percent,
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that tiny percentage of tweets remaining
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works out to roughly
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150,000 per month.
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The sheer scale of what we're dealing with
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makes for a challenge.
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You know what else makes my role
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particularly challenging?
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People do weird things.
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(Laughter)
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And I have to figure out what they're doing,
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why, and whether or not there's risk involved,
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often without much in terms of context
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or background.
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I'm going to show you some examples
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that I've run into during my time at Twitter --
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these are all real examples —
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of situations that at first seemed cut and dried,
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but the truth of the matter was something
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altogether different.
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The details have been changed
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to protect the innocent
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and sometimes the guilty.
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We'll start off easy.
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["Yo bitch"]
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If you saw a Tweet that only said this,
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you might think to yourself,
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"That looks like abuse."
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After all, why would you want to receive the message,
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"Yo, bitch."
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Now, I try to stay relatively hip
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to the latest trends and memes,
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so I knew that "yo, bitch"
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was also often a common greeting between friends,
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as well as being a popular "Breaking Bad" reference.
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I will admit that I did not expect
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to encounter a fourth use case.
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It turns out it is also used on Twitter
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when people are role-playing as dogs.
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(Laughter)
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And in fact, in that case,
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it's not only not abusive,
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it's technically just an accurate greeting.
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(Laughter)
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So okay, determining whether or not
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something is abusive without context,
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definitely hard.
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Let's look at spam.
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Here's an example of an account engaged
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in classic spammer behavior,
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sending the exact same message
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to thousands of people.
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While this is a mockup I put together using my account,
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we see accounts doing this all the time.
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Seems pretty straightforward.
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We should just automatically suspend accounts
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engaging in this kind of behavior.
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Turns out there's some exceptions to that rule.
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Turns out that that message could also be a notification
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you signed up for that the International Space Station is passing overhead
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because you wanted to go outside
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and see if you could see it.
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You're not going to get that chance
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if we mistakenly suspend the account
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thinking it's spam.
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Okay. Let's make the stakes higher.
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Back to my account,
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again exhibiting classic behavior.
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This time it's sending the same message and link.
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This is often indicative of something called phishing,
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somebody trying to steal another person's account information
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by directing them to another website.
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That's pretty clearly not a good thing.
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We want to, and do, suspend accounts
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engaging in that kind of behavior.
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So why are the stakes higher for this?
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Well, this could also be a bystander at a rally
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who managed to record a video
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of a police officer beating a non-violent protester
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who's trying to let the world know what's happening.
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We don't want to gamble
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on potentially silencing that crucial speech
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by classifying it as spam and suspending it.
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That means we evaluate hundreds of parameters
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when looking at account behaviors,
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and even then, we can still get it wrong
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and have to reevaluate.
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Now, given the sorts of challenges I'm up against,
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it's crucial that I not only predict
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but also design protections for the unexpected.
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And that's not just an issue for me,
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or for Twitter, it's an issue for you.
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It's an issue for anybody who's building or creating
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something that you think is going to be amazing
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and will let people do awesome things.
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So what do I do?
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I pause and I think,
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how could all of this
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go horribly wrong?
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I visualize catastrophe.
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And that's hard. There's a sort of
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inherent cognitive dissonance in doing that,
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like when you're writing your wedding vows
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at the same time as your prenuptial agreement.
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(Laughter)
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But you still have to do it,
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particularly if you're marrying 500 million tweets per day.
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What do I mean by "visualize catastrophe?"
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I try to think of how something as
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benign and innocuous as a picture of a cat
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could lead to death,
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and what to do to prevent that.
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Which happens to be my next example.
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This is my cat, Eli.
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We wanted to give users the ability
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to add photos to their tweets.
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A picture is worth a thousand words.
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You only get 140 characters.
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You add a photo to your tweet,
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look at how much more content you've got now.
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There's all sorts of great things you can do
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by adding a photo to a tweet.
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My job isn't to think of those.
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It's to think of what could go wrong.
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How could this picture
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lead to my death?
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Well, here's one possibility.
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There's more in that picture than just a cat.
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There's geodata.
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When you take a picture with your smartphone
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or digital camera,
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there's a lot of additional information
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saved along in that image.
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In fact, this image also contains
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the equivalent of this,
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more specifically, this.
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Sure, it's not likely that someone's going to try
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to track me down and do me harm
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based upon image data associated
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with a picture I took of my cat,
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but I start by assuming the worst will happen.
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That's why, when we launched photos on Twitter,
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we made the decision to strip that geodata out.
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(Applause)
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If I start by assuming the worst
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and work backwards,
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I can make sure that the protections we build
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work for both expected
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and unexpected use cases.
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Given that I spend my days and nights
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imagining the worst that could happen,
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it wouldn't be surprising if my worldview was gloomy.
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(Laughter)
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It's not.
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The vast majority of interactions I see --
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and I see a lot, believe me -- are positive,
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people reaching out to help
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or to connect or share information with each other.
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It's just that for those of us dealing with scale,
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for those of us tasked with keeping people safe,
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we have to assume the worst will happen,
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because for us, a one-in-a-million chance
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is pretty good odds.
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Thank you.
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(Applause)
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