‘Fiction is outperforming reality’: how YouTube’s algorithm distorts truth

Those are not easy questions to answer. Like all big tech companies, YouTube does not allow us to see the algorithm that shape our lives. They are secret formulas, proprietary software, and merely select technologists are entrusted to work on the algorithm. Guillaume Chaslot, a 36 -year-old French computer programmer with a PhD in artificial intelligence, was one of those engineers.

During the three years he worked at Google, he was placed for several months with a team of YouTube technologists working on the recommendation system.The experience resulted him to conclude that the priorities YouTube gives its algorithm are dangerously skewed.

” YouTube is something that looks like reality, “but its” distorted to build you expend more day online ,” he tells me when we meet in Berkeley, California.” The recommendation algorithm is not optimising for what is truthful, or balanced, or healthy for republic .”

Chaslot explains that the algorithm never remains the same. It is constantly changing the weight it gives to different signals: the viewing patterns of a user, for example, or the length of period a video is watched before someone clicks away.

The technologists he worked with is in charge of continuously experimenting with new formulas that would increase advertising revenues by extending the amount of time people watched videos. “Watch time was the priority,” he recollects.” Everything else was considered a distraction .”

Chaslot was fired by Google in 2013, ostensibly over performance issues. He insists he was let go after agitating for change within the company, use his personal time to team up with like-minded technologists to propose changes that could diversify the content people see.

He was especially worried about the aberrations that might result from a simplistic focus on depicting people videos they discovered irresistible, generating filter bubbles, for example, that only show people content that reinforces their existing view of the world. Chaslot said none of his proposed fixes were is under consideration by his directors.” There are many ways YouTube can change its algorithms to suppress fake news and improve the quality and diversity of videos people insure ,” he says.” I tried to change YouTube from the inside but it didn't work .”

YouTube told me that its recommendation system had evolved since Chaslot ran at the company and now” goes beyond optimising for watchtime “. The company said that in 2016 it started taking into account user ” satisfaction”, by using surveys, for example, or looking at how many “likes” a video received, to” ensure people were satisfied with what they were viewing “. YouTube added that additional changes had been implemented in 2017 to improve the news content surfaced in searches and recommendations and discourage the promotion of videos containing” inflammatory religion or supremacist” content.

It did not say why Google, which acquired YouTube in 2006, waited over a decade to build those changes. Chaslot believes such changes are mostly cosmetic, and have failed to fundamentally alter some disturbing biases that have evolved in the algorithm. In the summer of 2016, he built a computer program to investigate.

The software Chaslot wrote was designed to provide the world's first window into YouTube's opaque recommendation engine. The program simulates the behaviour of a user who starts on one video and then follows the chain of recommended videos- much as I did after watching the Logan Paul video- tracking data along the way.

It determines videos through a word search, selecting a “seed” video to begin with, and recording several layers of videos that YouTube recommends in the” up next” column. It does so with no viewing history, ensuring the videos being seen are YouTube's generic recommendations, rather than videos personalised to a user. And it repeats the process thousands of hours, accumulating layers of data about YouTube recommendations to build up a picture of the algorithm's preferences.

Over the last 18 months, Chaslot has use the program to explore bias in YouTube content promoted during the course of its French, British and German elections, global warming and mass shootings, and published his findings on his website, Algotransparency.com. Each analyze procures something different, but the research suggests YouTube systematically amplifies videos the hell is divisive, sensational and conspiratorial.

When his program determined a seed video by searching the query” who is Michelle Obama ?” and then followed the chain of” up next” suggestions, for example, the majority of members of the recommended videos said she “is a man”. More than 80% of the YouTube-recommended videos about the pope detected by his program described the Catholic leader as “evil”, ” satanic”, or” the anti-Christ “. There were literally millions of videos uploaded onto YouTube to satiate the algorithm's craving for content claiming the earth is flat.” On YouTube, fiction is outperforming reality ,” Chaslot says.

He believes one of the most shocking examples was detected by his program in the run-up to the 2016 general elections. As he observed in a short, largely unnoticed blogpost published after Donald Trump was elected, the impact of YouTube's recommendation algorithm was not neutral during the presidential race: it was pushing videos that were, in the main, helpful to Trump and damaging to Hillary Clinton.” It was strange ,” he explains to me.” Wherever you started, whether it was from a Trump search or a Clinton search, the recommendation algorithm was much more likely to push you in a pro-Trump direction .”

Trump won the electoral college as a result of 80,000 votes spread across three swaying nations. There were more than 150 million YouTube users in the US. The videos contained in Chaslot's database of YouTube-recommended election videos were watched, in total, more than 3bn periods before the vote in November 2016.

Even a small bias in the videos would have been significant.” Algorithms that shape the content we see can have a lot of impact, particularly on people who have not made up their intellect ,” says Luciano Floridi, a prof at the University of Oxford's Digital Ethics Lab, who analyses the ethics of artificial intelligence.” Gentle, implicit, quiet nudging can over period edge us toward choices we might not have otherwise attained .”

Promoting conspiracy theories

Chaslot sent me a database of more YouTube-recommended videos his program identified in the three months leading up to the presidential election. It contained more than 8,000 videos- all of them detected by his program look” up next” on 12 dates between August and November 2016, after equal numbers of searches for “Trump” and “Clinton”.

It was not a comprehensive set of videos and it may not have been a perfectly representative sample. But it was, Chaslot said, a previously unseen dataset of what YouTube was recommending to people interested in content about the candidates- one snapshot, in other words, of the algorithm's preferences.

Jonathan Albright, research director at the Tow Center for Digital Journalism, who reviewed the code used by Chaslot, tells it is a relatively straightforward piece of software and a reputable methodology.” This research captured the apparent direction of YouTube's political ecosystem ,” he says.” That has not been did before .”

I spent weeks watching, sorting and categorising the trove of videos with Erin McCormick, an investigative reporter and expert in database analysis. From the start, “were in” stunned by how many extreme and conspiratorial videos had been recommended, and the fact that almost all of them appeared to be directed against Clinton.

Some of the videos YouTube was recommending were the sort we had expected to see: broadcasts of presidential debates, Tv news clips, Saturday Night Live sketches. There were also videos of speeches by the two candidates- although, we procured, the database contained far more YouTube-recommended speeches by Trump than Clinton.

But what was most compelling was how often Chaslot's software detected anti-Clinton conspiracy videos appearing” up next” beside other videos.

There were dozens of clips stating Clinton had had a mental breakdown, reporting she had syphilis or Parkinson's disease, accusing her of having secret sexual relationships, including with Yoko Ono. Many were even darker, fabricating the contents of WikiLeaks disclosures to construct unfounded claims, accusing Clinton of involvement in assassinations or connecting her to satanic and paedophilic cults.

One video that Chaslot's data indicated was pushed especially hard by YouTube's algorithm was a bizarre, one-hour movie claiming Trump's rise was predicted in Isaiah 45. Another was entitled:” BREAKING: VIDEO SHOWING BILL CLINTON RAPING 13 YR-OLD WILL PLUNGE RACE INTO CHAOS ANONYMOUS CLAIMS “. The recommendation engine appeared to have been particularly helpful to the Alex Jones Channel, which broadcasts far-right conspiracy theories under the Infowars brand.

There were too many videos in the database for us to watch them all, so we focused on 1,000 of the top-recommended videos. We sifted through them one by one to determine whether the content was likely to have benefited Trump or Clinton. Just over a third of the videos were either unrelated to the election or contained content that was broadly neutral or even-handed. Of the remaining 643 videos, 551 were videos preferring Trump, while only merely 92 preferred the Clinton campaign.

The sample we had looked at suggested Chaslot's conclusion was correct: YouTube was six times more likely to recommend videos that aided Trump than his adversary. YouTube presumably never programmed its algorithm to benefit one candidate over another. But based on this evidence, at least, that is exactly what happened.

‘Leading people down hateful rabbit holes'

” We have a great deal of respect for the Guardian as a news outlet and organization ,” a YouTube spokesperson emailed me after I forwarded them our findings.” We strongly disagree, however, with the methodology, data and, most importantly, the conclusions built in their research .”

The spokesperson added:” Our search and suggestions systems reflect what people search for, the number of videos available, and the videos people choose to watch on YouTube. That's not a bias towards any particular candidate; that is a reflection of viewer interest .”

It was a curious reaction. YouTube seemed to be saying that its algorithm was a neutral mirror of the desires of the people who use it- if we don't like what it does, we have ourselves to blame. How does YouTube interpret” viewer interest”- and aren't” the videos people choose to watch” influenced by what the company shows them?

Offered the choice, we may instinctively click on a video of a dead man in a Japanese wood, or a fake news clip claiming Bill Clinton raped a 13 -year-old. But are those in-the-moment impulses actually a reflect of the content we want to be fed?

Tufekci, the sociologist who some months ago advised about the impact YouTube may have had on the election, tells me YouTube's recommendation system has probably figured out that edgy and hateful content is engaging.” This is a bit like an autopilot cafeteria in local schools that has figured out children have sweet teeth, and also like fatty and salty foods ,” she tells.” So you make a line offering such food, automatically loading the next plate as soon as the bag of chips or candy in front of the young person has been consumed .”

Once that get normalised, however, what is fractionally more edgy or bizarre becomes, Tufekci says , fiction and interesting.” So the food get higher and higher in sugar, fat and salt- natural human cravings- while the videos recommended and auto-played by YouTube get more and more bizarre or hateful .”

But why would a bias toward ever more weird or divisive videos benefit one candidate over another? That depends on the candidates. Trump's campaign was nothing if not weird and divisive. Tufekci points to surveys showing that” field of misinformation” largely tilted anti-Clinton before the election.” Fake news providers ,” she says,” found that fake anti-Clinton material played much better with the pro-Trump base than did fake anti-Trump material with the pro-Clinton base .”

She adds:” The topic before us is the ethics of leading people down hateful rabbit holes full of misinformation and lies at scale simply because it works to increase the time people spend on the site- and it does work .”

Tufekci was one of several academics I shared our research with. Philip Howard, a prof at the Oxford Internet Institute, who has examined how disinformation spread during the election, was another. He questions whether a further factor might have been at play.” This is important research because it seems to be the first systematic look into how YouTube may have been manipulated ,” he says, creating the possibility that the algorithm was gamed as part of the same propaganda campaigns that prospered on Twitter and Facebook.

In evidence to the House intelligence committee, analyse Russian interference in the election, Google's general counsel, Kent Walker, played down the degree to which Moscow's propaganda efforts infiltrated YouTube. The company's internal investigation had only identified 18 YouTube channels and 1,100 videos suspected of being linked to Russia's disinformation campaign, he told the committee in December- and generally the videos had relatively small numbers of views. He added:” We believe that the activity we received was limited because of various precautions that we had in place in advance of the 2016 election, and the fact that Google's products didn't lend themselves to the kind of micro-targeting or viral dissemination that these actors seemed to favor .”

Walker made no mention of YouTube recommendations. Correspondence made publicly available just last week, however, reveals that Senator Mark Warner, the ranking Democrat on the intelligence committee, afterwards wrote to the company about the algorithm, which he said seemed” particularly susceptible to foreign influence “. The senator demanded to know what the company was specifically doing to prevent a” malign incursion” of YouTube's recommendation system. Walker, in his written answer, offered few specifics, but told YouTube had” a sophisticated spam and security violate detecting system to identify anomalous behaviour and malignant incursions “.

Tristan Harris, a former Google insider turned tech whistleblower, likes to describe Facebook as a” living, breathing crime scene for what happened in the 2016 election” that federal researchers have no access to. The same might be said of YouTube. About half the videos Chaslot's program detected being recommended during the election have now faded from YouTube- many of them taken down by their inventors. Chaslot has always guessed this suspicious. These were videos with titles such as” Must Watch !! Hillary Clinton tried to ban this video”, watched millions of periods before they disappeared.” Why would someone take down a video that has been viewed millions of hours ?” he asks.

Quick guide

What you need to know about the Trump-Russia inquiry

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How serious are the allegations ?

The story of Donald Trump and Russia comes down to this: a sitting chairwoman or his campaign is suspected of having coordinated with a foreign country to manipulate a US election. The narrative could not be bigger, and the stakes for Trump- and the country- could not be higher.

What are the key questions ?

Investigators are asking two basic questions: did Trump's presidential campaign collude at any level with Russian operatives to sway the 2016 US presidential election? And did Trump or others transgress the law to throw investigators off the road?

What does the country think ?

While a majority of the American public now believes that Russia tried to disrupt the US election, opinions about Trump campaign involvement tend to split along partisan lines: 73% of Republican, but merely 13% of Democrats, believe Trump did” nothing wrong” in his dealings with Russia and its president, Vladimir Putin.

What are the implications for Trump ?

The affair has the potential to eject Trump from office. Experienced legal commentators believe that attorneys are investigating whether Trump committed an blockage of justice. Both Richard Nixon and Bill Clinton- the only presidents to face impeachment proceedings in the last century- were accused of obstruction of justice. But Trump's fate is probably up to the voters. Even if strong evidence of wrongdoing by him or his cohort emerged, a Republican congressional majority would probably block any action to remove him from office.( Such an action would be a historical rarity .)

What has happened so far ?

Former foreign policy adviser George Papadopolous pleaded guilty to perjury over his contacts with Russians linked to the Kremlin, and the president's former campaign administrator Paul Manafort and another aide face charges of fund laundering.

When will the inquiry come to an end ?

The investigations have an open timeline.

I situated a transcript of” This Video Will Get Donald Trump Elected”, a viral sensation that was watched more than 10 m days before it faded from YouTube. It was a benign-seeming montage of historical footage of Trump, accompanied by soft piano music. But when I played the video in slow motion, I watched that it contained weird flashings of Miley Cyrus licking a mirror. It seemed an amateurish and bizarre attempt at inserting subliminal, sexualised imagery. But it underscored how little oversight we have over any person who is want to use YouTube to influence public opinion on a vast scale.

I shared the entire database of 8, 000 YouTube-recommended videos with John Kelly, the chief executive of the commercial analytics firm Graphika, which has been tracking political disinformation campaigns. He ran the list against his own database of Twitter accounts active during the election, and concluded many of the videos appeared to have been pushed by networks of Twitter sock puppets and bots controlled by pro-Trump digital consultants with” a presumably unsolicited assistance” from Russia.

” I don't have smoking-gun proof of who logged in to control those accounts ,” he says.” But judging from the history of what we've seen those accounts doing before, and the characteristics of how they tweet and interconnect, they are assembled and controlled by someone- someone whose job was to elect Trump .”

Chaslot and some of the academics I spoke to felt this social media activity was significant. YouTube's algorithm may have developed its biases organically, but could it also have been nudged into spreading those videos even further?” If a video starts skyrocketing, there's no question YouTube's algorithm is going to start pushing it ,” Albright says.

YouTube did not deny that social media propaganda might have influenced its recommendations, but played down the likelihood, emphasizing its system “does not optimise” for traffic from Twitter or Facebook.” It seems as if the Guardian is attempting to shoehorn research, data and their conclusions into a common narrative about the role of technology in last year's election ,” the spokesman added.” The reality of how our systems run, however, simply don't subsistence this premise .”

After the Senate's correspondence with Google over possible Russian interference with YouTube's recommendation algorithm was made public last week, YouTube sent me a new statement. It emphasised changes it induced in 2017 to discourage the recommendation system from promoting some types of problematic content.” We appreciate the Guardian's work to shine a spotlight on this challenging issue ,” it added.” We know there is more to do here and we're looking forward to attaining more announcements in the months ahead .”

Content creators

With its flashy graphics and slick-haired anchor, the Next News Network has the appearances of a believable news channel. But behind the facade is a dubious operation that recycles tales harvested from far-right publishings, fake news sites and Russian media outlets.

The channel is run by anchor Gary Franchi, once a leading supporter of a conspiracy that claimed the US government was creating concentration camp for its citizens. It was the Next News Network that broadcast the fabricated claims about Bill Clinton raping a adolescent, although Franchi insists he is not a fake news producer.( He tells me he prefers to see his channel as” commentating on conservative news and sentiment “.)

In the months leading up to the election, the Next News Network was transformed into a factory of anti-Clinton news and opinion, creating dozens of videos a day and reaching an audience comparable to that of MSNBC's YouTube channel.

Chaslot's research indicated Franchi's success could largely be credited to YouTube's algorithms, which consistently amplified his videos to be played” up next “. YouTube had sharply dismissed Chaslot's research.

I contacted Franchi to see who was right. He sent me screen grabs of the private data given to people who upload YouTube videos, including a breakdown of how their audiences detected their clips. The most important source of traffic to the Bill Clinton rape video, which was viewed 2.4 m days in the month leading up to the election, was YouTube recommendations.

The same was true of all but one of the videos Franchi sent me data for. A typical example was a Next News Network video entitled ” WHOA! HILLARY THINKS CAMERA'S OFF … SENDS SHOCK MESSAGE TO TRUMP “ in which Franchi, pointing to a tiny movement of Clinton's lips during a Tv debate, claims she says “fuck you” to her presidential rival. The data Franchi shared revealed in the month leading up to the election, 73% of the traffic to the video- is tantamount to 1.2 m of its views- was due to YouTube recommendations. External traffic accounted for only 3% of the views.

Franchi is a professional who makes a living from his channel, but many of the other inventors of anti-Clinton videos I spoke to were amateur sleuths or part-time conspiracy theoreticians. Typically, they might receive a few hundred views on their videos, so they were shocked when their anti-Clinton videos started to receive millions of views, as if they were being pushed by an invisible force.

In every lawsuit, the most important source of traffic- the invisible force-out- came from the clips appearing in the” up next” column. William Ramsey, an occult researcher from southern California who attained ” Irrefutable Proof: Hillary Clinton Has a Seizure Disorder !”, shared screen grabs that depicted the recommendation algorithm pushed his video even after YouTube had emailed him to say it violated its guidelines. Ramsey's data presented the video was watched 2.4 m times by US-based users before election day.” For a nobody like me, that's a lot ,” he says.” Enough to sway the election, right ?”

Daniel Alexander Cannon, a conspiracy theoretician from South Carolina, tells me:” Every video I put out about the Clintons, YouTube would push it through the roof .” His best-performing clip was a video titled ” Hillary and Bill Clinton' The 10 Photos You Must Find ‘”, essentially a slideshow of appalling( and apparently doctored) images of the Clintons with voiceover in which Cannon theorizes on their own health. It has been find 3.7 m times on YouTube, and 2.9 m of those views, Cannon told, came from” up next ” recommendations.

Chaslot has put a spotlight on a trove of anti-Clinton conspiracy videos that had been hidden in the shadows- unless, that is, you were one of the the millions YouTube served them to. But his research also does something more important: uncovering how thoroughly our lives are now mediated by artificial intelligence.

Less than a generation ago, the way voters viewed their legislators was largely shaped by tens of thousands of newspaper editors, journalists and Tv executives. Today, the invisible codes behind the big technology platforms have become the new kingmakers.

They pluck from oblivion people like Dave Todeschini, a retired IBM engineer who, “let off steam” during the election by recording himself opining on Clinton's supposed involvement in paedophilia, child sacrifice and cannibalism.” It was crazy, it was nuts ,” he told of the avalanche of traffic to his YouTube channel, which by election day had more than 2m positions.

” Breaking news ,” he announced in one of his last dispatches before the vote: the FBI, he told, had just procured graphic images of Clinton and her aide in” sexually compromising positions” with a teen.” It seems to me, with Bill Clinton's trip-ups to paedophile island a number of hours, that what we have here is nothing short of the Clinton paedophile ring ,” he proclaimed.

Todeschini sits in his darkened living room in New Jersey, staring into his smartphone.” I'll tell you what: the rabbit hole just got a couple of yards deeper .”

Contact the author: paul.lewis @theguardian. com.

A full description of the methodology Chaslot are applied to detect YouTube recommendations( and an explanation of how the Guardian analysed them) is available here.

Read more: www.theguardian.com