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YouTube users watch a billion hours of video each day \u2014 from branded content and tons of music videos to cats and conspiracy theories.<\/p>\n
But one thing that has long plagued content creators is the mysterious algorithm<\/strong>. Like other platforms, YouTube aims to show users what they want to see, using an automated system that sorts through the massive selection of content uploaded every second.<\/p>\n According to YouTube, their search and discovery system<\/a> has two goals in mind. One is to help viewers find videos that they want to watch.<\/strong> Two is to maximize viewer engagement and satisfaction<\/strong> in the long term.<\/p>\n But it seems that there\u2019s more at play here, from how the platform makes recommendations to who gets access to ad revenue.<\/p>\n Here\u2019s a little more about what\u2019s going on behind the scenes.<\/p>\n Initially, YouTube cared about clicks and clicks alone.<\/strong> Which, of course, was a flawed metric. Counting each time viewers clicked “play” did not indicate quality, and creators started to take advantage of the system by adding click bait descriptions to drum up higher rankings.<\/p>\n Then, in 2012, YouTube changed it up. Instead of relying on clicks, they opted to measure engagement<\/strong><\/a> \u2014 or how long people spent on a video. Which, of course, is a more accurate measure of whether videos meet viewer expectations.<\/p>\n In 2016, YouTube released a paper discussing how the algorithm worked. It\u2019s geared toward a more technical audience, but long story short, the algorithm looks at how the audience interacts<\/strong> with video content, using AI that analyzes 80 billion pieces of audience feedback.<\/p>\n Among those billions, audience feedback covers likes, dislikes, watch time, how many videos someone watches, and what they do or don\u2019t watch.<\/strong><\/p>\n These days, they\u2019re playing around with the order videos appear in an attempt to boost viewer satisfaction. However, the inner workings are still a big mystery to content creators and viewers alike.<\/p>\n Pew Research<\/a><\/span> found that 64 percent of recommendations went to videos with more than a million views. And the top 50 most recommended videos were viewed on average, 456M times a pop.<\/p>\n As such, the research concluded that when YouTube doesn\u2019t know much about your preferences, they\u2019ll go ahead and recommend what\u2019s popular. And, the more you watch, the suggested videos start to get longer and longer.<\/p>\n See, each researcher took a “walk” through the platform, each starting from a different point of entry.<\/p>\n On average, those initial videos were about 9 minutes. Videos at the end of the session were, on average 14 minutes and 50 seconds \u2014 and had a much higher view count.<\/p>\n It seems that YouTube assumed that if a user watches multiple videos, they\u2019re likely to hang around for a while.<\/p>\n Additionally, the research found that only about five percent of the recommendations offered went to videos with fewer than 50,000 views.<\/p>\n That said, the Pew Research was performed by anonymous users who did not have an existing user history. YouTube makes recommendations based on a user\u2019s habits<\/strong>, more or less getting to know them over time.<\/p>\n So, when the algorithm is working with a clean slate, it\u2019s going to lump you in with the masses until you start offering up preferences \u2014 likes, dislikes, specific searches, and so on.<\/p>\n Last year, several YouTube creators criticized the platform for experimenting with the video delivery system. YouTube stars have had issues with videos failing to display for subscribers or the platform pulling ads from the videos people do see.<\/p>\n The company started testing an algorithm that changed the order<\/strong> that videos appeared in users\u2019 feeds. Where videos used to display in a subscribers\u2019 feed in chronological order, YouTube said it was testing an approach aimed at delivering content that users actually wanted to watch.<\/p>\n The other major change came in the form of an ad disabling effort, where YouTube\u2019s AI started flagging offensive content.<\/strong><\/p>\n The so-called ad-pocalypse\u00a0was a response to advertiser complaints about their products shown alongside videos containing hate speech, violence, and other offensive content.<\/p>\n The problem, however, is many YouTubers found themselves demonetized, ads pulled due to colorful language<\/span><\/a> \u2014 not hate speech or violence. While some of these creators may have violated specific rules, the platform failed to explain how creators can protect themselves<\/span><\/a> or fix the situation.<\/p>\n While the published paper we mentioned above represented an act of transparency, content creators have long been frustrated with a lack of insight into how the platform works.<\/p>\n In response to the ad issues and surprise display changes, YouTubers have increasingly started posting longer videos to squeeze in more ads per video. The idea is, the algorithm will give preference to longer videos, as they make more money in ad dollars.<\/p>\n How to edit and share video game footage on Youtube<\/p>\r\n Read now \u25ba<\/a>\r\n <\/div>\r\n <\/div>\r\n <\/a>\r\n <\/div>\r\n<\/div>\n Take, for example, this article in The Atlantic<\/span><\/a>\u00a0that poses the question, \u201cdoes YouTube unwittingly radicalize its viewers?\u201d In it, they cite the scholar, Zeynep Tufekci, who studies sociology in the age of the internet. She found that after watching Donald Trump rallies on the platform that autoplay started recommending videos that took a sharp turn away from mainstream political content \u2014 think Holocaust denials and conspiracy theories.<\/strong><\/p>\n Then, she tried watching videos of Bernie Sanders and Hilary Clinton. And again, found that autoplay began recommending conspiracy videos<\/strong>, this time with a leftist bent.<\/p>\n The same thing happened with apolitical content \u2014 looking up videos about jogging eventually led viewers to a selection of videos about running ultramarathons.<\/p>\n Tufekci\u2019s theory is the algorithm is exploiting a natural human desire to dig deeper, to uncover secrets \u2014 sending viewers toward an extremist menu of content.<\/p>\n Okay, time for yet another pivot. YouTube said today<\/a> that it will try to show fewer fringe videos. It won’t remove the\u00a0clips, but the algorithm will try to serve them up less frequently as “watch next” options.<\/p>\n The Pew Report we mentioned above also mentions that half of YouTube users they surveyed use YouTube to understand the world around them. Which, when you consider the fact that 68% of Americans<\/span><\/a> get at least some news from social media, that may be a real problem.<\/p>\n How to keep your kids safe on YouTube<\/p>\r\n Read now \u25ba<\/a>\r\n <\/div>\r\n <\/div>\r\n <\/a>\r\n <\/div>\r\n<\/div>\n At a certain point, algorithms start taking on a life of their own, taking what they\u2019ve learned in the programming stage and from user behavior and kind of, well, running with it. We doubt the platform is actively trying to instill extremist behavior in its users or prevent creators from appearing in the suggested results.<\/p>\n But, it is a problem when you consider that some ads may have been pulled unfairly. It\u2019s also a problem that some people believe the platform is a reliable source of information.<\/p>\n You might be able to learn how to grill salmon in a pan or DIY a new coffee table, but it\u2019s maybe not the best place to learn about global events or your choice of political candidates.<\/p>\nHow the YouTube algorithm works<\/h2>\n
The algorithm has gone through a few iterations<\/h3>\n
Pew finds that popularity and length play a major role<\/h3>\n
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<\/p>\nLack of transparency has been a problem for content creators<\/h3>\n
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\r\n <\/div>\r\n \r\n The algorithm can send viewers on a dark path<\/h3>\n
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<\/p>\n\r\n \r\n \r\n \r\n \r\n <\/div>\r\n \r\n Chances are YouTube doesn\u2019t fully understand the algorithm, either<\/h3>\n