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. 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 of the fake-news ecosystem.
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 such as email.
Note: This was originally published at Fortune, where I was a senior writer from 2015 to 2017
Albright said this led him deeper into the traffic-analysis and social mapping process, in which he tried to dig down to see which of the top sites were driving the most traffic and who else they were connected to via Facebook and Twitter. As he 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.”
In a follow-up post, entitled “The #Election2016 Micro-Propaganda Machine,” Albright did what he called a “medium-scale data analysis,” crawling and indexing 117 websites that are known to be associated with the propagation of fake news content.
Using open-source software, Albright indexed every website and extracted web addresses up to one level deep. He said this pulled in the majority of links on these sites (735,263 of them in total, over more than 400,000 pages). He then mapped the connections between the sites and plotted them as dots, based on the strength of their connections.
Albright subsequently expanded his sample to include more than 300 sites, including some prominent distributors such as Breitbart News. In total, he collected more than 1.3 million URLs that were shared on these sites, and analyzed the incoming and outgoing traffic.
Two of the largest hubs he found in the network 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 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, and a sense of how the various fake-news distribution or creation sites are inter-connected — and 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.”
Even if Facebook and Google are the largest distributors of fake news or disinformation because of their size, Albright argues that his work provides a scientifically-based outline or overview of what the supply chain underneath that distribution system looks like. And that could help determine who the largest players are and what their purpose is.