目前，圍繞著假新聞在何種程度上幫助特朗普贏得了競選的爭論仍在發(fā)酵，很多人都認(rèn)為互聯(lián)網(wǎng)上存在著一個松散的右翼網(wǎng)站聯(lián)盟，它就是通過各個社交網(wǎng)絡(luò)平臺大量轉(zhuǎn)發(fā)這些假新聞的主要推手。猜測歸猜測，卻很少有人嘗試使用科學(xué)的方法去探究這一問題。

北卡羅來納州伊隆大學(xué)教授喬納森·奧爾布萊特是一名數(shù)據(jù)新聞領(lǐng)域的專家，曾為谷歌和雅虎工作過，主要從事媒體分析和社交網(wǎng)絡(luò)方面的研究。近日，他圍繞互聯(lián)網(wǎng)上的假新聞生態(tài)系統(tǒng)繪制了一張“假新聞傳播路線圖”。

由于Facebook和谷歌的巨大體量，它們當(dāng)之無愧地成為了假新聞最大的兩個傳播途徑。不過奧爾布萊特的研究卻以科學(xué)的方法使我們得以一覽這些傳播途徑背后的假新聞供應(yīng)鏈。或許這將有助于我們理解誰才是炮制假新聞的最大推手，以及他們的目的究竟是什么。

奧爾布萊特表示，他的研究方法首先是考察某些最大的假新聞傳播站點(diǎn)的網(wǎng)絡(luò)訪問量。奧爾布萊特在發(fā)表于medium.com上的一篇文章中指出，即便谷歌和Facebook決定禁止假新聞網(wǎng)站使用他們的廣告網(wǎng)絡(luò)，但仍不足以解決假新聞泛濫的問題。

奧爾布萊特指出，這是由于大量進(jìn)出這些網(wǎng)站的訪問量都是有機(jī)地實(shí)現(xiàn)的。而這些假新聞網(wǎng)站之所以在谷歌的搜索引擎和Facebook的動態(tài)消息里排在前面，也是由于這個原因。很多假新聞的傳播是通過各個老式網(wǎng)站之間的共享實(shí)現(xiàn)的。也就是說，它們是通過電子郵件發(fā)送的。

這也促使奧爾布萊特更深入地進(jìn)行了訪問量分析和社交網(wǎng)絡(luò)繪圖工作。他想確定哪些大網(wǎng)站吸引了最多的訪問量，以及它們又通過Facebook和Twitter等社交網(wǎng)絡(luò)與其他哪些網(wǎng)站產(chǎn)生了聯(lián)系。奧爾布萊特在文中這樣寫道：

“谷歌的廣告網(wǎng)絡(luò)和Facebook的最新動態(tài)所使用的‘相關(guān)文章’算法，會放大假新聞給人帶來的情感沖擊，而社交媒體天生就具有放大政治戾氣的能力。不過我認(rèn)為，不論是新聞記者、研究人員還是數(shù)據(jù)專家，他們首先應(yīng)該關(guān)注：1、與內(nèi)容生產(chǎn)有的因素；2、與網(wǎng)絡(luò)訪問量有關(guān)的因素。”

接下來，奧爾布萊特進(jìn)行了一輪“中等規(guī)模的數(shù)據(jù)分析”，探索分析了117個已知的與假新聞有關(guān)的網(wǎng)站。隨后他發(fā)表了一篇名叫《2016美國大選微宣傳機(jī)器》(The #Election2016 Micro-Propaganda Machine)的文章，通過圖表指出了這些網(wǎng)站之間的聯(lián)系。并且根據(jù)它們之間的聯(lián)系的強(qiáng)弱，將這它們畫成了以下大小不一的圓點(diǎn)。

As debate continues over the extent to which “fake news” helped Donald Trump win the presidential race, many have talked about a network of loosely-affiliated, right-wing sites that distributed this content through social media platforms. But few have tried to describe it in scientific terms.

Jonathan Albright, a professor at Elon University in North Carolina, is an expert in data journalism who has worked for both Google and Yahoo. He specializes in media analytics and social networks, and he has created a network map or topology that describes the landscape of the fake-news ecosystem.

Even if Facebook and Google are the largest distributors of fake news or disinformation because of their size, Albright’s work arguably provides a scientifically-based overview of the supply chain underneath that distribution system. And that could help determine who the largest players are and what their purpose is.

Albright says his research started with a look at the traffic generated by some of the top fake-news distribution sites. As he described in a post published on Medium, he came to the conclusion that banning them from ad networks run by Google or Facebook wouldn’t solve the problem.

That’s because much of the traffic to and from those sites—and therefore their presence at the top of Google’s search engine or high up in the Facebook news feed—is achieved organically, he argued. Many seemed to be driven primarily by sharing through old-fashioned networks. In other words: they’re sent via email.

This led Albright deeper into the traffic-analysis and social mapping process. He tried to determine which of the top sites were driving the most traffic and who else they were connected to via Facebook and Twitter. As Albright described it:

Google’s ad network and Facebook’s News Feed/“Related Stories” algorithms amplify the emotional spread of misinformation, and social media naturally turn up the volume of political outrage [but] I think journalists, researchers and data geeks should first look into the factors that are actually 1) producing the content and 2) driving the online traffic.

Next Albright did what he called a “medium-scale data analysis,” crawling and indexing 117 websites that are known to be associated with fake news. In a follow-up post, entitled The #Election2016 Micro-Propaganda Machine, he mapped the connections between those sites and plotted them as dots, based on the strength of their connections.

隨后，奧爾布萊特又將他的樣本擴(kuò)展到了300多個網(wǎng)站，其中包括布萊巴特新聞網(wǎng)（Breitbart News）等一些知名的新聞傳播渠道。他總共收集分析了130多萬個進(jìn)出這些網(wǎng)站的外鏈。

第一眼看見奧爾布萊特的這張網(wǎng)絡(luò)地圖，很多人都會產(chǎn)生這樣一種印象，即有些“節(jié)點(diǎn)”或“樞紐”推動了大量與假新聞有關(guān)的訪問量。但圖中也有數(shù)量極多的網(wǎng)站是不少人說不定連聽都沒聽說過的。

在奧爾布萊特發(fā)現(xiàn)的兩個最大的“樞紐”中，其中之一是一個名叫“保守百科”(Conservapedia)的網(wǎng)站——它相當(dāng)于是左翼版的維基百科，另一個網(wǎng)站名叫Rense，這兩者都吸引了大量的訪問流量。這張圖上其他比較知名的節(jié)點(diǎn)還包括布萊巴特新聞網(wǎng)、每日傳訊（DailyCaller）和YouTube等等。（有些假新聞網(wǎng)站可能想通過YouTube這個渠道利用他們的訪問量賺錢。）

奧爾布萊特表示，他特別注意不去深究到底是什么人炮制了這些假新聞。不過《華盛頓郵報(bào)》最近發(fā)表的一篇報(bào)道引述了一個沒有什么名氣的團(tuán)體的說法，稱炮制這波假新聞的幕后黑手是一個俄羅斯的行動網(wǎng)絡(luò)——不過這則分析并非很有說服力。

奧爾布萊特表示，他只是想了解一下這個問題的波及面，以及各個假新聞的生產(chǎn)基地和傳播網(wǎng)站之間是如何互相聯(lián)系的。奧爾布萊特還想用公開數(shù)據(jù)和開源工具來進(jìn)行這項(xiàng)研究，這樣其他人也能構(gòu)建出類似的模型。他表示：

“在查無實(shí)據(jù)或證據(jù)不全的情況下主張一條新聞是‘假新聞’，這并不是最好的做法——實(shí)際上它可能是最糟糕的策略，因?yàn)樗M(jìn)一步加劇了當(dāng)前的喧囂。最近，我覺得很多記者和新聞機(jī)構(gòu)都是在針對大眾的反應(yīng)‘制造’新聞，而不是先行一步地解讀那些根本性的問題。”

有了以上這張假新聞生態(tài)系統(tǒng)的“地圖”，人們就能明白各個假新聞傳播節(jié)點(diǎn)之間的聯(lián)系，而谷歌、Facebook等大型社交平臺以及其他媒體機(jī)構(gòu)也就能更容易地追蹤假新聞的蔓延勢頭，提出可能的解決方案。（財(cái)富中文網(wǎng)）

譯者：樸成奎

Albright subsequently expanded his sample to include more than 300 sites, including some prominent distributors such as Breitbart News. In total, he collected and analyzed the incoming and outgoing traffic of more than 1.3 million URLs.

More than anything, the impression one gets from looking at Albright’s network map is that there are some extremely powerful “nodes” or hubs that propel a lot of the traffic involving fake news. And it also shows an entire universe of sites that many people have probably never heard of.

Two of the largest hubs Albright found were a site called Conservapedia—a kind of Wikipedia for the right wing—and another called Rense, both of which got huge amounts of incoming traffic. Other prominent destinations were sites like Breitbart News, DailyCaller and YouTube (the latter possibly as an attempt to monetize their traffic).

Albright said he specifically stayed away from trying to determine what or who is behind the rise of fake news. The Washington Post recently wrote about a report from a little-known group that says a network of Russian actors are behind the wave, although the analysis was fairly weak.

Instead, Albright said he just wanted to try and get a handle on the scope of the problem, as well as a sense of how the various fake-news distribution or creation sites are inter-connected. Albright also wanted to do so with publicly-available data and open-source tools so others could build on it. He said:

Reporting on ‘fake news’ with unsubstantiated claims and incomplete evidence isn’t the best approach—in fact, it’s probably the worst strategy, because it adds to the existing noise. Lately, I feel that many journalists and news organizations are churning out news in response to the public, rather than leading the way to inform them on the underlying issues.

With the landscape of the fake-news ecosystem outlined in terms of the connections between the various nodes, it may be easier for platforms like Google and Facebook—or even for other media outlets—to track the spread of the problem and come up with potential solutions.