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Del Harvey 談Twitter對特殊訊息的衡量

Del Harvey: The strangeness of scale at Twitter

 

Photo of three lions hunting on the Serengeti.

講者:Del Harvey

2014年3月攝於TED2014

 

翻譯:洪曉慧

編輯:朱學恒

簡繁轉換:洪曉慧

後制:洪曉慧

字幕影片後制:謝旻均

 

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關於這場演講

當每秒產生成千上萬條Twitter訊息的同時,百萬分之一的機率-包括看似無害、卻可能對用戶造成傷害的訊息-每天發生約500次。對領導Twitter交易安全小組的Del Harvey來說,這並非樂觀的情形。這位安全專家花大量時間思考,如何在給予全球大眾發言機會的同時,防止最壞情況的發生。藉由不動聲色的幽默,她讓聽眾一窺如何保護2.4億用戶安全的方法。

 

關於Del Harvey

Del Harvey是Twitter信任與安全部門副總裁。

 

為什麼要聽她演講

在Twitter,Del Harvey的工作是確保用戶安全及安全感,平衡Twitter的廣大空間,對抗垃圾訊息發送者、騷擾者及更糟的情況,創造可行的策略,使Twitter訊息流暢地傳遞。在加入這個蓬勃發展的社交媒體網站之前,她在一個反兒童剝削團體擔任了五年執法聯絡人員,共事對象包括當地警察部門、FBI(聯邦調查局)、美國法警及特勤局等機構。

 

隨著Twitter的成長,其充滿創意的用戶(他們因自行想出許多Twitter重要功能而聞名)發現過度分享的新方法,冒犯及騷擾了其他人。Harvey及團隊的挑戰是,在剷除最糟情況的同時,使這個網站感覺像一個安全的地方,可進行我們目前在這個網站進行的新型態對話。

 

Del Harvey 的英語網上資料

Twitter: @delbius

 

[TED科技‧娛樂‧設計]

已有中譯字幕的TED影片目錄(繁體)(簡體)。請注意繁簡目錄是不一樣的。

 

Del Harvey 談Twitter對特殊訊息的衡量

 

我在Twitter的工作是確保用戶的信賴,捍衛用戶權益,保護用戶之間、有時是用戶本身的安全。我們談談Twitter的訊息規模。2009年1月,我們每天在平台上看見超過兩百萬條新Twitter訊息;2014年1月則超過5億條。在六分鐘內,我們看見兩百萬條新Twitter訊息,那是24,900%的成長。

 

目前,Twitter上絕大多數活動不會對任何人造成傷害、不涉及任何風險。我的工作是剷除及預防可能造成風險的活動。聽起來十分簡單,對嗎?你或認為這十分容易,因為我剛說過,Twitter上絕大多數活動不會對任何人造成傷害。為何花這麼多時間在無害的活動中尋找潛在危機?以Twitter的訊息規模來看,百萬分之一的機率代表一天發生500次,與其它接收類似訊息規模的公司相同。對我們來說,那些罕見、不太可能發生的極端案例如同家常便飯。假設99.999%的Twitter訊息不會對任何人造成風險、不涉及任何威脅:也許人們正在記錄旅遊景點,例如澳洲的心型礁堡;或描述他們正在參加的演唱會;或分享可愛小動物的照片。當你剔除那99.999%之後,剩下那些比例極微的Twitter訊息,計算結果大約是一個月15萬條。這龐大的規模對我們來說是個挑戰。

 

你們知道還有什麼使我的工作充滿挑戰?人們做奇怪的事(笑聲)。我必須弄清他們在做什麼、為何這麼做,以及是否涉及風險,在對來龍去脈所知不多的情況下。我打算展示幾個我任職Twitter期間遇到的例子-這些都是真實案例-乍看之下似乎是司空見慣的情形,但事情的真相則完全不同。其中的細節有所更動,以保護無辜者,有時是犯罪分子。我們從簡單的開始:

 

〔Yo bitch〕(bitch有母狗、婊子等義)

 

如果你看見一條Twitter訊息只有這句話,你或許認為:「那似乎是一種侮辱。」畢竟,誰會希望收到這種訊息:「Yo, bitch」。現在,我試著跟上最新趨勢及流行語的腳步,因此我知道「Yo, bitch」通常也是朋友間常用的問候語;也是《絕命毒師》中的流行說法。我得承認,我不曾料到會遇見第四種用法:事實上這也是人們在Twitter中扮演狗的角色時的用語(笑聲)。因此事實上,在那種情況下,這不僅不是侮辱,理論上來說只是一個準確的問候。(笑聲)

 

因此在不知來龍去脈的情況下決定某些訊息是否出於惡意相當困難。

 

我們來看一下垃圾訊息。這是某個帳戶涉及散佈垃圾訊息的典型範例-向數千人發送完全相同的訊息。這是我用我的帳號拼湊而成的範例。我們目睹某些帳戶總是從事這種行為。似乎相當簡單明瞭,我們應該自動停用涉及這種行為的帳戶。事實上其中存在某些例外情況。事實上那些訊息也可能是通知:你為了目睹國際太空站掠過上空而進行登記,因為你希望能及時到戶外碰碰運氣。如果我們誤以為它是垃圾訊息而將那個帳戶停用,你將失去這個機會。

 

好,我們看看更高的風險。回到我的帳號,同樣顯示常見的行為。這次是發送相同的訊息和連結,這通常意味著所謂的網路釣魚:有人試圖竊取另一個帳戶的資訊,藉由將他們導向另一個網站。顯然那不是什麼好事,我們希望、也確實停用了從事那種行為的帳戶。因此為何這種做法的風險更高?好,這也可能是某個集會中的旁觀者,設法錄下警察毆打非暴力抗議者的過程,試著讓全世界知道發生了什麼事。我們不想因將它歸類為垃圾訊息、停用這個帳戶,而冒著隱藏這個重要訊息的風險。這意味著當我們觀察帳戶行為時,得評估數百個因素。即使如此,我們仍可能犯錯,必須重新評估。

 

好,鑒於這些我面臨的挑戰,重要的是,我不僅得進行預測,也得設計出對例外情況的保護。那不僅是與我或Twitter有關的問題,也是與你們有關的問題,對任何建立或創造非凡事物、使人們做出非凡之舉的人的來說,這也是問題。因此我怎麼做?我停下來思考:一切怎會如此糟糕?我想像災難的發生。這相當困難,彷彿某種與生俱來的認知失調在作怪;就像一面撰寫結婚誓言、一面撰寫婚前協議(笑聲)。但你還是得做,尤其是當你一天得面對5億條Twitter訊息時。我所謂的「想像災難」是指什麼?我試著想像,某種如貓照片般溫和無害的東西如何可能導致死亡,以及如何預防這種情況。這正是我的下一個例子。這是我的貓,Eli,我們希望給予用戶將圖片加入Twitter的能力。一張圖片能代表千言萬語;Twitter訊息僅能輸入140個字元。你在Twitter中加入一張圖片,看看現在增加了多少內容。藉由將圖片加入Twitter,你可達成各種美妙的事。我的工作不是思考那些事,而是思考可能發生什麼問題。

 

這張圖片如何能導致我的死亡?好,有一個可能性:圖片中包含的資訊不僅是貓,還有地理資訊。當你用智慧手機或數位相機拍攝照片時,隨著照片儲存了許多額外資訊。事實上這張照片也包含了相當於這個的資訊;更具體地說是這個。當然,不太可能有人試圖追蹤我、傷害我,藉由附加在我所拍攝的貓照片中的資訊。但我由假設將發生最壞的情況開始,這就是為何當我們將圖片載入Twitter時,我們決定刪除那些地理資訊(掌聲)。如果我從最壞的假設開始,然後反向推理,我可確認我們建立的保護機制適用於預料中及預料之外的情況。

 

鑒於我日夜想像可能發生最壞的情況,如果我的世界觀有些陰鬱並不令人驚訝(笑聲)。並非如此。我目睹絕大多數的互動-我見過太多了,相信我-是正面的。人們伸出援手或彼此聯繫、分享資訊。只因我們面對相當龐大的規模,肩負保障大眾安全的責任,我們必須假設最壞的情況。因為對我們來說,百萬分之一的機率是相當大的賭注。

 

謝謝。

 

(掌聲)

 

以下為系統擷取之英文原文

About this Talk

When hundreds of thousands of Tweets are fired every second, a one-in-a-million chance — including unlikely sounding scenarios that could harm users — happens about 500 times a day. For Del Harvey, who heads Twitter’s Trust and Safety Team, these odds aren’t good. The security maven spends her days thinking about how to prevent worst-case scenarios while giving voice to people around the globe. With deadpan humor, she offers a window into how she keeps 240 million users safe.

About the Speaker

Del Harvey is the VP of Trust & Safety at Twitter. Full bio

Transcript

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)


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