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  • 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

  • particularly challenging?

  • People do weird things.

  • (Laughter)

  • 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

  • or background.

  • 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

  • altogether different.

  • The details have been changed

  • to protect the innocent

  • and sometimes the guilty.

  • We'll start off easy.

  • ["Yo bitch"]

  • 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,

  • "Yo, bitch."

  • 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.

  • (Laughter)

  • And in fact, in that case,

  • it's not only not abusive,

  • it's technically just an accurate greeting.

  • (Laughter)

  • So okay, determining whether or not

  • something is abusive without context,

  • definitely hard.

  • 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.

  • (Laughter)

  • 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.

  • There's geodata.

  • 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.

  • (Applause)

  • 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.

  • (Laughter)

  • It's not.

  • 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.

  • Thank you.

  • (Applause)

My job at Twitter

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【TED】Del Harvey: The strangeness of scale at Twitter

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    CUChou   に公開 2014 年 12 月 25 日
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