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你有沒有在社交媒體被網絡暴民圍攻過？我有過。去年12月我發了一條推特諷刺白人優越論者大衛·杜克，結果他的支持者一擁而上，把我的推特生生變成了骯臟的下水道，充斥著納粹般瘋狂又難聽的人身攻擊言論，而且持續了好幾天。

沒人能戰勝互聯網上的暴民。只要有點仇恨和謾罵的火種，社交媒體上的人們只能棄械投降，網站也只好撤下評論功能。誰想生活在全是馬屁精和瘋子的網絡社區里呢？

幸運的是，現在下悲觀結論可能為時過早。一項新策略有望解決暴民問題，恢復互聯網文明討論的氛圍。這個項目由谷歌母公司Alphabet旗下的智庫Jigsaw主導，主要依靠人工智能手段，可以解決以往無法審核海量評論的頭疼問題。

為了解釋Jigsaw的具體做法，首席研究科學家盧卡斯·迪克森將網絡暴民問題與所謂的拒絕服務攻擊相比較，拒絕服務供給是指攻擊者故意用垃圾信息淹沒網站，導致服務器過載最后下線。

“網絡暴民的區別是不會用垃圾信息攻擊網站，而是攻擊評論區或是社交媒體賬戶或話題標簽，結果是其他人一句話都插不上，暴民掌握全部話語權。”迪克森表示。

大量惡意評論不僅會對個人造成困擾，對媒體公司和零售商也是威脅，因為現在很多商業模式都圍繞著網絡社區展開。Jigsaw研究網絡暴民時，已經開始量化損失。舉個例子，如果有維基百科（Wikipedia）的編輯受到人身攻擊，Jigsaw會測算其后該編輯在維基百科上貢獻詞條頻率與受攻擊之間的關系。

要解決當前扭曲的在線討論氛圍，根源還在于海量數據和深度學習，這也是人工智能領域發展迅速的一塊，主要目標是模仿人體大腦的神經網絡。近來深度學習已經在谷歌翻譯工具上實現了了不起的突破。

說到評論，Jigsaw讓機器學習《紐約時報》（New York Times）和維基百科里的上千萬條評論，學會識別言辭中的攻擊性以及文不對題的發帖。直接影響是：《紐約時報》之類的網站之前只有能力處理10%的文章評論，但在采用新算法后可以實現100%覆蓋。

雖然每家媒體評論區的調性和詞匯差別可能很大，但Jigsaw表示可以調整審核工具，適用各種網站。這就意味著即便是小博主或網絡零售商，也能放心放開評論功能，不用擔心被網絡暴民攻陷。

技術愛好者都很關注Jigsaw的動向。最近，《連線》雜志（Wired）上的一篇文章將Jigsaw的新項目稱為“互聯網正義聯盟”，還夸贊了谷歌旗下一系列行善的項目。

但也有些專家表示，Jigsaw團隊可能低估了問題的難度。

最近比較高調的機器學習項目主要關注點在圖片識別和翻譯文本上。但互聯網的對話經常很看語境：舉例來說，很明顯應該讓機器學習項目從所有評論中屏蔽“賤貨”，但有時人們用到這個詞并無惡意，卻同樣會被算法屏蔽，比如有人會說“生活就像個賤貨。”或“其實我本來不想抱怨工作的，但是……”想教會機器從模糊的語境中辨清真實意思其實并不容易。

“機器學習能學會語言規范，但沒法理解文字背后的語境和感情，尤其是像推特這么簡短的文字。這是人類終其一生才能學會的東西。”前谷歌軟件工程師大衛·奧爾巴哈表示。他補充說，Jigsaw的項目可以向《紐約時報》之類的網站提供更好的審核工具，但到了推特和Reddit等更自由的論壇，能發揮的作用就不大了。

種種質疑并未讓Jigsaw的迪克森退縮。他指出，網絡暴民跟拒絕服務攻擊一樣都是永遠無法徹底解決的問題，但其影響是可以減弱的。迪克森相信，Jigsaw利用機器學習技術方面的最新成果可以控制網絡暴民的威力，讓和平討論重獲優勢。

Jigsaw的研究人員還指出，看起來像暴民團伙的攻擊——即突然跳出來一起罵臟話的一群人經常是個人行為，有時是某些組織設置的自動程序模仿暴民團伙。Jigsaw的識別工具正飛速學習迅速識別并阻止這些行為。

此外，有人質疑道高一尺魔高一丈，網絡暴民會根據審核工具的特點調整謾罵方式，從而避開屏蔽，迪克森對此也有解釋。

“審核工具越多，攻擊的花招必然也會越多，”迪克森表示。“理想情況是攻擊方式花哨到沒人看得懂，沒人能懂也就沒效果，那么攻擊自然會停止。”

那些被社交媒體暴民趕走的人們

2015年到2016年

從NPR到路透，越來越多的大眾媒體網站和博客關停了評論功能。

Have you ever been attacked by trolls on social media? I have. In December a mocking tweet from white supremacist David Duke led his supporters to turn my Twitter account into an unholy sewer of Nazi ravings and disturbing personal abuse. It went on for days.

We’re losing the Internet war with the trolls. Faced with a torrent of hate and abuse, people are giving up on social media, and websites are removing comment features. Who wants to be part of an online community ruled by creeps and crazies?

Fortunately, this pessimism may be premature. A new strategy promises to tame the trolls and reinvigorate civil discussion on the Internet. Hatched by Jigsaw, an in-house think tank at Google’s parent company, Alphabet, the tool relies on artificial intelligence and could solve the once-impossible task of vetting floods of online comments.

To explain what Jigsaw is up against, chief research scientist Lucas Dixon compares the troll problem to so-called denial-of-service attacks in which attackers flood a website with garbage traffic in order to knock it off-line.

“Instead of flooding your website with traffic, it’s flooding the comment section or your social media or hashtag so that no one else can have a word, and basically control the conversation.” says Dixon.

Such surges of toxic comments are a threat not only to individuals, but also to media companies and retailers—many of whose business models revolve around online communities. As part of its research on trolls, Jigsaw is beginning to quantify the damage they do. In the case of Wikipedia, for instance, Jigsaw can measure the correlation between a personal attack on a Wikipedia editor and the subsequent frequency the editor will contribute to the site in the future.

The solution to today’s derailed online discourse lies in reams of data and deep learning, a fast-evolving subset of artificial intelligence that mimics the neural networks of the brain. Deep learning gave rise to recent and remarkable breakthroughs in Google’s translation tools。

In the case of comments, Jigsaw is using millions of comments from the New York Times and Wikipedia to train machines to recognize traits like aggression and irrelevancy. The implication: A site like the Times, which has the resources to moderate only about 10% of its articles for comments, could soon deploy algorithms to expand those efforts 10-fold.

While the tone and vocabulary on one media outlet comment section may be radically different from another’s, Jigsaw says it will be able to adapt its tools for use across a wide variety of websites. In practice, this means a small blog or online retailer will be able to turn on comments without fear of turning a site into a vortex of trolls.

Technophiles seem keen on what Jigsaw is doing. A recent Wired feature dubbed the unit the “Internet Justice League” and praised its range of do-gooder projects.

But some experts say that the Jigsaw team may be underestimating the challenge.

Recent high-profile machine learning projects focused on identifying images and translating text. But Internet conversations are highly contextual: While it might seem obvious, for example, to train a machine learning program to purge the word “bitch” from any online comment, the same algorithm might also flag posts in which people are using the term more innocuously—as in, “Life’s a bitch.” or “I hate to bitch about my job, but?…” Teaching a computer to reliably catch the slur won’t be easy.

“Machine learning can understand style but not context or emotion behind a written statement, especially something as short as a tweet. This is stuff it takes a human a lifetime to learn.” says David Auerbach, a former Google software engineer. He adds that the Jigsaw initiative will lead to better moderation tools for sites like the New York Times but will fall short when it comes to more freewheeling forums like Twitter and Reddit.

Such skepticism doesn’t faze Jigsaw’s Dixon. He points out that, like denial-of-service attacks, trolls are a problem that will never be solved but their effect can be mitigated. Using the recent leaps in machine learning technology, Jigsaw will tame the trolls enough to let civility regain the upper hand, Dixon believes.

Jigsaw researchers also point out that gangs of trolls—the sort that pop up and spew vile comments en masse—are often a single individual or organization deploying bots to imitate a mob. And Jigsaw’s tools are rapidly growing adept at identifying and stifling such tactics.

Dixon also has an answer to the argument that taming trolls won’t work because the trolls will simply adapt their insults whenever a moderating tool catches on to them.

“The more we introduce tools, the more creative the attacks will be,” Dixon says. “The dream is the attacks at some level get so creative no one understands them anymore and they stop being attacks.”?

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Driven from social media by trolls

2015–16

Increasingly, popular media sites and blogs, from NPR to Reuters, are eliminating comments from their pages.

2015年7月

Reddit爆發了一場用臨時首席執行官愛倫·鮑的話說“史上最嚴重的網絡暴民攻擊”，隨后愛倫·鮑宣布辭職。

July 2015

Ellen Pao, interim CEO of Reddit, resigns in the wake of what she calls “one of the largest trolling attacks in history.”

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2016年7月

網絡暴民在電影演員萊斯利·瓊斯的推特賬號下發了一大堆種族歧視和色情圖片，之后萊斯利宣布退出推特。她在最后幾條推文里寫道，“你們想象不到有多么惡毒。”

本文另一版本刊發于2017年2月1日出版的《財富》雜志上，標題為《網絡暴民獵手》。 （財富中文網）

譯者：夏林

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July 2016

Movie actress Leslie Jones quits Twitter after trolls send a barrage of racist and sexual images. In one of her final tweets, she writes, “You won’t believe the evil.”

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A version of this article appears in the February 1, 2017 issue of Fortune with the headline "Troll Hunters."