research paper youtube algorithm

How the YouTube Algorithm Works in 2024

Looking to increase your YouTube video views? Step one: find out what’s new with the YouTube algorithm and how it ranks your content.

Table of Contents

If you believe in free will, we have terrible news — well, at least when it comes to YouTube. Because YouTube’s algorithm for recommendations drives 70% of what people watch on the platform .

That is some seriously staggering influence!

So it’s no surprise that marketers, influencers, and creators are obsessed with unlocking the secret of the Youtube algorithm. How does it work? What makes it tick? And, most importantly, how can we take advantage of this mysterious formula?

Well, wonder no more, because in this blog post, we’ll cover everything about the YouTube algorithm that you’ve been dying to know.

Bonus: Download the free 30-day plan to grow your YouTube following fast , a daily workbook of challenges that will help you kickstart your Youtube channel growth and track your success. Get real results after one month.

A brief history of the YouTube algorithm

What is the YouTube algorithm? To answer that question, let’s do a quick overview of how YouTube’s Algorithm has changed over the years. And how it works today.

2005-2011: Optimizing for clicks & views

According to founder Jawed Karim (a.k.a. the star of Me at the Zoo ), YouTube was created in 2005 in order to crowdsource the video of Janet Jackson and Justin Timberlake’s notorious Superbowl performance . So it makes sense that YouTube’s algorithm started off by recommending videos that attracted the most views or clicks.

Of course, this led to an increase in misleading titles and thumbnails (a.k.a. clickbait). User experience plummeted as videos left people feeling tricked, unsatisfied, or plain old annoyed.

2012: Optimizing for watch time

In 2012, YouTube adjusted its recommendation system to support time spent watching each video. It also included time spent on the platform overall. When people find videos valuable and interesting, they watch them for longer. Or, so the theory goes.

This shift to reward watch time was a game changer. According to Mark Bergan , author of Like, Comment, Subscribe: Inside YouTube’s Chaotic Rise to World Domination, “[Watch time] had an immediate impact. Early YouTubers were basically making TikTok videos… but watch time created gaming, beauty vlogging, alt-right podcasts… all these verticals we now associate with YouTube.”

Accounts that were big performers previously (like videos from eHow, or MysteryGuitarMan) dropped off almost immediately.

YouTube’s algorithm change led some creators to try to make their videos shorter in order to make it more likely viewers would watch to completion. Others made their videos longer in order to increase watch time overall. YouTube didn’t comment on either of these tactics and maintained the party line: make videos your audience wants to watch, and the algorithm will reward you.

That said, as anyone who has ever spent any time on the internet knows, time spent is not necessarily equivalent to quality time spent. Soon, YouTube changed tack again.

2015-2016: Optimizing for satisfaction

In 2015, YouTube began measuring viewer satisfaction directly with user surveys. It also prioritized direct response metrics like Shares, Likes, and Dislikes (and, of course, the especially brutal “not interested” button).

In 2016, YouTube released a whitepaper describing some of the inner workings of its AI: Deep Neural Networks for YouTube Recommendations.

In short, the algorithm had gotten way more personal. The goal was to find the video each particular viewer wants to watch, not just the video that lots of other people have perhaps watched in the past.

As a result, in 2018, YouTube’s Chief product officer mentioned on a panel that 70% of watch time on YouTube is spent watching videos the algorithm recommends.

Create. Schedule. Publish. Engage. Measure. Win.

2016-present: Dangerous content, demonetization, and brand safety

Over the years, YouTube’s size and popularity have resulted in an increasing number of content moderation issues. And what the algorithm recommends has become a concerning topic not just for creators and advertisers but for journalists and the government as well.

YouTube has said it is serious about its responsibility to support a diverse range of opinions while reducing the spread of harmful misinformation. Algorithm changes enacted in early 2019, for example, have reduced consumption of borderline content by 70% . (YouTube defines borderline content as content that doesn’t quite violate community guidelines but is harmful or misleading. Violative content, on the other hand, is immediately removed .)

This issue affects creators, who fear accidentally violating ever-changing community guidelines. Or being punished with strikes, demonetization, or worse.

(Former CEO Susan Wojcicki said one of YouTube’s priorities in 2021 was increasing transparency for community guidelines for creators).

It also affects brands and advertisers, who don’t want their name and logo running alongside white supremacists.

Meanwhile, American politicians are increasingly concerned with the societal role of social media algorithms. YouTube (and other platforms) have been summoned to account for their algorithms at Senate hearings. And in early 2021 Democrats introduced a ”Protecting Americans from Dangerous Algorithms Act.”

In recent years, researchers have found the new YouTube algorithm has made strides to reduce the amount of harmful content its algorithm serves up. Though, the recent 2024 Finnish election found evidence of YouTube promoting alt-right content — despite purported changes to the algorithm.

It seems we’re not out of the harmful-YouTube-content woods, just yet.

How does the YouTube algorithm work in 2024?

Next, let’s talk about what we know about how the YouTube algorithm works.

Currently, the YouTube algorithm delivers distinct recommendations to each user. These recommendations are tailored to users’ interests and watch history and weighted based on factors like the videos’ performance and quality.

When deciding what to recommend to each user, the YouTube algorithm takes into account the following:

• What videos have they enjoyed in the past? If you’ve watched a 40-minute video essay about the flags of the world or gave it a like or comment, it’s probably safe to say you found it interesting. Expect more flag content coming your way.
• What topics or channels have they watched previously? If you subscribe to the Food Network’s YouTube channel, the algorithm will likely show you more cooking content.
• What videos are typically watched together? If you watch “How to change a monster truck tire,” and most people who watch that also watch “Monster truck repair 101,” YouTube might recommend that as follow up viewing.

That’s why a Millennial music-lover beauty-queen has a homepage that looks like this:

Of course, YouTube wants to recommend relevant, quality videos to each of its precious users. It’s not exactly a positive experience to follow a suggestion to watch “The World’s 36 Most Stylish Cats” and find it boring, low-quality or weirdly racist.

So how does YouTube evaluate if a video is worthy of recommendation?

I t’s not about the content. The actual content of your video is not evaluated by the YouTube algorithm at all. Videos about how great YouTube is aren’t more likely to go viral than a video about how to knit a beret for your hamster.

“Our algorithm doesn’t pay attention to videos; it pays attention to viewers. So, rather than trying to make videos that’ll make an algorithm happy, focus on making videos that make your viewers happy,” says YouTube .

Instead, YouTube looks at the following metrics for its recommendation algorithm:

• Do people actually watch it? When a video is recommended, do people actually watch it, ignore it, or click “not interested”?
• How long do people watch it? The YouTube algorithm looks at both the view duration and the average percentage viewed to inform the ranking.
• Did viewers like it? Likes and dislikes are evaluated, as are engagement rates and post-watch survey results.
• What is your regional context? The time of day and the language you speak also influence the YouTube algorithm.

How YouTube determines the algorithm

More than 500 hours of content are uploaded to YouTube every single minute. Imagine a world without the YouTube algorithm trying to help you find the most relevant content. One word comes to mind: chaos.

That’s why it’s important to understand that the goal of YouTube’s algorithm isn’t to bring you the most popular or the most recent video on your search term. The goal is to bring you the video that you specifically will find the most useful.

That’s why two different YouTube users searching for the same term may see a totally different list of results .

YouTube’s search algorithm prioritizes the following elements :

• Relevance: The YouTube algorithm tries to match factors like title, tags, content, and description to your search query.
• Engagement: Signals include watch time and watch percentage, as well as likes, comments, and shares.
• Quality: To evaluate quality, the algorithm looks at signals to determine the channel’s authority and trustworthiness on a given topic.
• User search and watch history: What have you enjoyed or viewed in the past? This will impact which search results the YouTube algorithm will assume will be helpful.

These factors are combined in slightly different ways, depending on where on YouTube you are receiving recommendations.

YouTube recommends videos in three different places on the platform.

This is what you see when you open up the YouTube app or visit the YouTube website. It’s personalized to each viewer. The recommendation engine selects videos for the Home screen based on:

• Performance of the video
• Watch and search history of the user

Suggested videos

These are the videos recommended alongside the video you’re already watching. The algorithm suggests videos here based on:

• The topic of the current video
• The viewer’s watch history

Each user’s search results will be slightly different thanks to the personalized signals the algorithm takes into account. These signals include:

• The relevance of the title, description, and video content to the search term
• Performance and engagement of video

What is the YouTube Shorts algorithm?

One of the newest formats to enter the YouTube ecosystem is YouTube Shorts . These short, vertical videos created using a smartphone and uploaded directly to YouTube from the YouTube app, like Stories or TikTok videos.

YouTube Shorts have taken the content world by storm. In fact, nearly 70 billion YouTube viewers are watching Shorts daily. So, don’t sleep on this new format.

Now that you know Shorts are great, the question is: how do you get your Shorts discovered?

Well, according to Todd Sherman, the product lead for Shorts, the algorithm for Shorts is different from regular YouTube. Instead of users picking videos to watch, they swipe through content, so the algorithm focuses on showing a variety of videos to keep everyone interested.

Unlike some platforms where just looking at the first frame counts as a view, Shorts requires viewers to actually want to watch, although they won’t say exactly how much. They’re keeping this threshold secret to prevent people from trying to manipulate the system.

Creators are advised to focus on storytelling rather than sticking to a specific video length , even though most Shorts are still kept under a minute. Custom thumbnails are discouraged for Shorts, and while hashtags can be helpful, their impact can vary.

Timing your uploads and the quantity of Shorts you post aren’t crucial factors for optimization , according to YouTube. It’s more about putting out quality content. Shorts might initially get a lot of attention, but their popularity can taper off based on audience reception. YouTube discourages deleting and reposting Shorts repeatedly, as it could be seen as spammy behavior.

Hot tip: You can schedule your YouTube videos via the Hootsuite Dashboard so you have time to focus on more spontaneous YouTube Shorts on the go.

In the future, YouTube intends to introduce features allowing Shorts creators to link to longer videos, showing their commitment to integrating rather than replacing long-form content. Additionally, they’re testing a feature to group uploads from prolific channels, making it easier for viewers to explore content without overwhelming their feed.

Here’s a quick breakdown of what the YouTube Shorts algorithm takes into account:

• Relevance: Do the title, tags, content, and description match the search term?
• Engagement: Do other people like and comment on this video?
• User watch history: What have you enjoyed or viewed in the past?
• Similar content: What other Shorts do similar audiences like to watch?
• Watch time: Less important than for classic videos. But if someone can’t even sit through a 15-second video, that’s probably not a good sign.

Paige Cooper is the Hootsuite Inbound YouTube Lead. She runs Hootsuite Labs , our Youtube channel and she sees Shorts as an opportunity ripe for the taking.

“The rise of vertical video hasn’t changed the main algorithm per se, but YouTube Shorts are creating a big new opportunity for creators,” she says. “If you’re already running an Instagram Reels or TikTok strategy , publishing on YouTube Shorts seems to be an easy win.”

16 tips to improve your organic reach on YouTube

While there are no YouTube algorithm instructions, remember that the algorithm follows the audience. If you already have a YouTube marketing plan in place, these tips will help you grow your channel’s views.

Cue: Eye of the Tiger. This is your YouTube algorithm training.

1. Do your keyword research

There’s no human being sitting at YouTube headquarters watching your video and ranking it.

Instead, the algorithm looks at your metadata as it decides what the video is about, which videos or categories it’s related to, and who might want to watch it.

When it comes to describing your video for the algorithm, you want to use accurate, concise language that people are already using when they search.

For example, if you were uploading a comedy sketch, you should probably include the words “comedy” and “funny” in the title and description and be crystal clear about the topics or subject of the video.

Because YouTube is a search engine as much as a video platform, you can conduct your keyword research in the same way you would for a blog post or web copy : using free tools like Google Keyword Planner or SEMrush.

Once you’ve identified your primary keywords, you’ll want to use them in four places:

• In the video’s file name (i.e., comedy-dad-jokes.mov)
• In the video’s title (using catchy natural language like “Real life dad does stand up comedy for first time”)
• In the YouTube video description (especially within the first two lines, above the fold)
• In the video’s script (and therefore in the video’s subtitles and closed captions—which means uploading an SRT file).

But there’s one place you don’t need to put your keywords:

• In the video’s tags. According to Youtube, tags “play a minimal role in video discovery” and are most helpful if your keyword or channel name is often misspelled. (i.e., standup, stand up, comedy, comedie, etc.) Adding excessive tags to your video description could even harm your video. It’s against YouTube’s policies on spam, deceptive practices, and scams .

Read more about social SEO and YouTube SEO to keep your knowledge brewing.

2. Make your thumbnails click-worthy

But without being clickbaity, obviously.

“Appeal” is the word YouTube uses to describe how a video entices a person to take a risk (albeit a minor one) and watch something new. While YouTube itself doesn’t care what your thumbnail looks like visually, it is keeping track of whether or not people actually click through .

YouTuber Joshua Weissman uses a consistent style for his thumbnails that usually feature his face, a succinct title, and intriguing imagery.

To maximize your video’s appeal:

• Upload a custom thumbnail (and keep the visual style consistent across all your thumbnails)
• Write an intriguing, catchy title—the kind you can’t not click on
• Remember the first sentence or so of the description will show up in search, so make it interesting and relevant.

Feeling like you need a YouTube algorithm tutorial? Check out more tactics to promote your YouTube channel .

3. Keep people watching your video, and all your videos

Once you have a viewer watching one video, make it easy for them to keep watching your content and stay within your channel’s ecosystem.

For instance, the end of Taskmaster episodes feature a card that links to more videos and a prompt to subscribe to the channel.

To keep viewers in your ecosystem, use:

• Cards: Flag relevant other videos in your video
• End screens: End with a CTA to watch another relevant video
• Playlists: Promote a list of topically similar videos
• Subscription watermarks: Allow users to subscribe to your channel within the video itself.

For more on converting viewers to subscribers, read our guide to getting more YouTube subscribers .

Pro Tip: Making a video series is a great way to capitalize on a recent spike in viewers. Using a scheduling tool like Hootsuite can make it easy to pre-plan your monthly factory tour or interview sessions in advance.

4. Attract views from other sources

Views that don’t come from the YouTube algorithm can still inform your success with the algorithm.

For example, you can attract views from YouTube ads , external sites, cross-promoting on social media , and partnerships with other channels or brands can all help you earn views and subscribers, depending on your strategy.

For instance, on the Murphy Beds Canada website, the support section links to a selection of videos that open in YouTube.

The algorithm won’t punish your video for having a lot of traffic from off-site (e.g., a blog post). This is important because click-through-rates and view duration often tank when the bulk of a video’s traffic is from ads or an external site.

According to YouTube’s product team, the algorithm only pays attention to how a video performs in context. So, a video that performs well on the homepage will be surfaced to more people on the homepage, no matter what its metrics from blog views look like.

Pro Tip: Embedding a YouTube video in your blog or website is great for both your blog’s Google SEO as well as your video’s view counts on YouTube.

5. Engage with comments and other channels

In order for your audience to grow , you need to nurture your relationships with your viewers. For many viewers, part of YouTube’s appeal is feeling closer to creators than they do to traditional celebrities.

Use Hootsuite Streams to stay on top of untagged mentions, and stay up to date about every conversation that effects your industry.

6. Don’t stoop to creating clickbait

Racking up views for the sake of views is a lose-lose situation. Maybe you’ve crafted the most titillating thumbnail-title combo of all time and are capturing an outsized amount of attention… but viewers will quickly figure out you’ve tricked them and bounce.

So what did that really gain you?

Not only will you have sullied your brand reputation with a bait-and-switch, you’ll also be punished by the YouTube algorithm. There’s no chance clickbait is going to impress the recommendation engine.

Stick to accurate, quality content, and create titles and thumbnails that properly represent what viewers are going to see.

The challenge is, as YouTuber Alec Wilcock says, “to make sure your videos are actually valuable for your audience. You can’t just want them to be valuable.”

“Viewers can see fluff or filler a mile away, so there’s no phoning it in, or you will see a drop in your watch time,” advises Hootsuite’s Paige Cooper. “It’s a cliche at this point, but every time you say ‘algorithm’ replace that word with ‘audience.’ We aren’t making videos for robots, we’re making them for smart, discerning people who have infinite other ways to spend their time. ” Ask yourself, “Would I watch this?” as much as possible.

7. Keep your eye on the conversation

Your YouTube channel can be a great way to hop on the bandwagon for trending topics. But it’s tough to make a clever response video or weigh in on an issue if you’re not paying attention to what’s going on.

Hootsuite’s keyword search streams are super helpful for social listening . Plug in an industry term or relevant hashtag to keep in the know about conversations in your community.

Creating compelling, relevant content is one of the best ways to impress that YouTube algorithm. One recent video that is doing super well for Hootsuite is our video on The fastest Hootsuite demo EVER (how to manage social media with Hootsuite) .

Hootsuite Streams and keyword research helped inform the strategy that led to this video being created. “We did the research to find a workaround for a common problem people have, and that paid off with a 78% percent retention rate,” Cooper explains.

Google Trends is another great source for keeping in the loop. If you notice a problem people are looking to solve, be the one to solve that problem.

8. Evolve by experimenting

The only way to know what really captures an audience’s attention and gets you that precious watch time is to try, try, try. You’ll never find that secret recipe for success without a little experimentation… and probably a few failures (a.k.a. learning opps) along the way.

Mr. Beast didn’t become the world’s richest YouTuber overnight. By trial and error, he discovered that the wilder and more extravagant his stunts were, the better his views and engagement did. And now he’s, uh, curing blindness. What a time to be alive!

“It’s the little changes and course corrections that add up over time!” says Cooper. “ As a small channel, obviously the dream is to create a piece of gold that goes viral. But as a small educational channel, focusing on practical, valuable videos that we know people already want is important.”

Two tactics that have paid off for Hootsuite Labs are 1) getting more specific (a.k.a. “niching down”) with a topic (i.e., rather than “Instagram vs. TikTok” going after “Instagram vs. TikTok for business”; and 2) being the first to make a video on a topic. “But really both of those mean knowing your audience: what they care about, what they’re problems are, what they’re curious about, and what they want to know,” says Cooper.

Take courage from the fact that if an experiment really bombs, that low-performing video won’t down-rank your channel or future videos in any way. (Unless you have truly alienated your audience to the point where they don’t want to watch you anymore.) Your videos all have an equal chance to earn viewers, according to YouTube’s product team .

9. Get to know your audience

It’s almost impossible to wow your audience if you don’t know who they are. That’s why understanding your target audience and their behavior is so important.

Get to know your YouTube audience by digging into your analytics, either via YouTube directly or using Hootsuite’s audience insights tool.

Understanding their location, their gender, and their age can help inform your content strategy . Watching how they actually interact with your videos—engagement, watch time, and all of those important social media metrics—also will point you in the right direction.

Knowledge! Is! Power!

10. Post at the best time

The YouTube algorithm doesn’t directly base its recommendations on what time or day you post. But the algorithm does take stock of a video’s popularity and engagement. And one surefire way to get more views on YouTube is to post your video when your audience is online.

Prep your videos in advance and then use a scheduling tool for maximum reach . The Hootsuite scheduler, for instance, provides custom recommendations for the best posting time for your audience. Here’s how it works:

11. Don’t just make long videos: make good videos

While the YouTube algorithm rewards watch time, it’s all relative. “Our discovery system uses absolute and relative watch time as signals when deciding audience engagement, and we encourage you to do the same,” says YouTube . “Broadly speaking, relative watch time is more important for short videos and absolute watch time is more important for longer videos.”

So think less about total length when you’re creating a video and more about creating compelling content that keeps the viewer watching through to the end , no matter how long or short your video is.

If they’re dropping off 25% of the way through, that’s not great, whether your video is 6 minutes or 60 minutes.

Pro tip: Check out your audience retention metric to help understand how long your unique viewers like to watch. Then you can adjust your content accordingly.

“You’re constantly learning about your audience, and every win and every loss will tell you something about what they value (or don’t value), which you can apply to your next video,” notes Cooper.

“If you’re losing fifty percent of your audience in the first 30 seconds, try cutting that content. If your average view time is two minutes out of 10, see what happens if you make a five-minute video. Each video is evaluated on its own merits, which means that each video is a new chance to succeed… or fail. (Sorry!)”

Mastering the YouTube algorithm is just one way to get your YouTube channel the attention it deserves , of course. For more on thriving on YouTube, check out our guide to building a custom YouTube marketing strategy . And, ahem, while you’re over there… maybe you’d like to give our channel a little like and subscribe ?

12. Get on the Shorts train

Short form video isn’t going away. In fact, many platforms, including Instagram and YouTube, are paying special attention to short videos — especially as TikTok continues its upward climb.

YouTube has made it clear that YouTube Shorts are its, “ number one area of focus .” In fact, the platform is seeing ad engagement on Shorts rising rapidly, while YouTube’s overall ad revenue is steadily declining — so it’s no big secret where YouTube executive heads are turning.

If you want to stand out on YouTube in 2024, you’re going to want to start posting YouTube Shorts.

Simple, short, and engaging, these quick videos can diversify your content stack on YouTube, and give the platform even more opportunities to rank and promote your channel.

Tired of posting your YouTube Shorts one by one? Quit stalling and start scheduling your YouTube Shorts with Hootsuite . Available on both desktop and mobile apps, Hootsuite makes it easy to plan, post, and analyze your YouTube content from a single dashboard.

Looking to make more money on your Shorts? Check out our YouTube monetization guide .

14. Make your videos accessible to everyone

Social media is used by a diverse and global audience. Your viewers likely come from different countries, backgrounds, and abilities — and you want your content to be easy to access no matter who they are.

Social media accessibility is the process of designing social media content to be inclusive to everyone, including those with disabilities.

On YouTube, this might look like including closed captioning in your videos, adding alt text to your YouTube thumbnails, or using descriptive captions that are easily read by screen readers.

Ignoring social media accessibility will close your content off to a wide range of viewers, which will lead to lower views, less engagement, and overall less boost from the YouTube algorithm.

15. Keep an eye on your best competitors

Sometimes, the best way to get inspiration is by checking out what your competitors are doing right… and wrong.

Does your primary competitor create similar content but get way more views? Maybe this is a sign to analyze their thumbnails, video descriptions, or dive deeper into their cross-promotion strategy.

Similarly, if you notice your videos consistently out-performing the competition, take note of what you’ve done differently lately, as well as what they are failing to do.

The more you know, the more you grow.

16. Analyze, analyze, analyze

As with everything on social media, data is your best friend. If your strategy is stuck, stunted, or stalled, it’s probably time to take a look at your analytics.

Were you performing better this time last year? Do you usually see a slump around the holidays? Maybe you stopped adding closed captions to your videos and are losing viewers because of this.

Without detailed analytics and data tracking, you’re only speculating. Tools like Hootsuite collect intricate data points about your YouTube analytics performance , and make them easy to view and understand in a simple dashboard.

Get clear charts, graphs, and numbers that you can then generate into reports to share with your wider team. Then, use the information gathered to better inform your YouTube videos going forward.

Let Hootsuite make growing your YouTube channel easier. Get scheduling, promotion, and marketing tools all in one place for your entire team. Sign up free today.

Grow your YouTube channel faster with Hootsuite . Easily moderate comments, schedule video, and publish to Facebook, Instagram, and Twitter.

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Hannah Macready is a freelance writer with 12 years of experience in social media and digital marketing. Her work has appeared in publications such as Fast Company and The Globe & Mail, and has been used in global social media campaigns for brands like Grosvenor Americas and Intuit Mailchimp. In her spare time, Hannah likes exploring the outdoors with her two dogs, Soup and Salad.

Paige Cooper is a lapsed librarian turned copywriter turned inbound marketing strategist who spends her days growing the Hootsuite Labs YouTube channel.

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Echo chambers, rabbit holes, and ideological bias: How YouTube recommends content to real users

Subscribe to techstream, megan a. brown , megan a. brown senior research engineer, center for social media and politics - new york university @m_dot_brown jonathan nagler , jonathan nagler professor of politics, co-director center for social media and politics - new york university @jonathan_nagler james bisbee , james bisbee assistant professor of political science - vanderbilt university @jamesbisbee angela lai , and angela lai ph.d. candidate - new york university @angelaight joshua a. tucker joshua a. tucker professor of politics, co-director center for social media and politics - new york university @j_a_tucker.

October 13, 2022

• 22 min read

Elon Musk’s recent effort to buy Twitter along with court fights over social media regulation in Florida and Texas have recharged the public conversation surrounding social media and political bias. Musk and his followers have suggested that Twitter release regular audits of Twitter’s algorithm— or that Twitter open source its algorithm —so independent parties can audit it for political bias.

Before Elon Musk entered the fray, however, a growing body of journalistic work and academic scholarship had begun to scrutinize the impact of social media platform algorithms on the type of content people see. On the one hand, most empirical research has found that user behavior, not recommendation algorithms, largely determines what we see online, and two recent studies disputed Musk’s claim of anti-conservative bias . On the other hand, disclosures from the Facebook Files last fall suggested that adjustments to Facebook’s algorithm amplified angry and polarizing content and may have helped foment the January 6 insurrection . Social media content feeds are crucial to media consumption today. By extension, then, it is critical to understand how the algorithms that generate our feeds shape the information we see.

In a new working paper , we analyze the ideological content promoted by YouTube’s recommendation algorithm. Multiple media stories have posited that YouTube’s recommendation algorithm leads people to extreme content. Meanwhile, other studies have shown that YouTube, on average, recommends mostly mainstream media content. In our study, which utilizes a new methodological approach that makes it easier for us to isolate the impact of the YouTube recommendation algorithm than previous work, we found that YouTube’s recommendation algorithm does not lead the vast majority of users down extremist rabbit holes, although it does push users into increasingly narrow ideological ranges of content in what we might call evidence of a (very) mild ideological echo chamber. We also find that, on average, the YouTube recommendation algorithm pulls users slightly to the right of the political spectrum, which we believe is a novel finding. In the remainder of this article, we lay out exactly why such research is important, how we did our research, and how we came to these conclusions.

YouTube’s Recommendation Engine: Why Is It Important? 

By many measures, mass polarization is on the rise in the United States. Americans are more willing to condone violence , less open to relationships that cut across party lines , and more prone to partisan motivated reasoning . We’ve seen two prime examples in the past two years. First, the nation’s response to COVID-19: Preventative measures such as mask wearing and vaccination became inextricably linked to partisanship . Even more dramatically, many Republicans claimed that the 2020 U.S. presidential elections (although not the concurrent legislative elections) were riddled with fraud , culminating in the January 6 Capitol attacks, while Democrats largely accepted the results as legitimate.

While few claim that social media actually is the root cause of political polarization, many worry that the affordances of social media are accelerating the more recent rise in political polarization. 1 One prominent concern is that our rapidly evolving information environment  has increased the number of ideological news outlets and made it easier for individuals to exist in “echo chambers” where they’re rarely confronted with alternative perspectives. Many believe that social media algorithms exacerbate this problem by suggesting content to users that they will enjoy. While this can be harmless or even beneficial in areas like sports or music, in areas such as news content, health content, and others, this type of personalization could lead to harmful societal outcomes, such as siloing individuals into anti-vaccine, extremist, or anti-democratic echo chambers.

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In our study, we focus on YouTube. YouTube, started in 2005 and acquired by Google in 2006, has grown to prominence as the internet’s archive for video content. Even before Facebook, Twitter, Reddit, or other platforms implemented algorithmically-generated user feeds, YouTube was providing users with recommended videos to watch next. By many measures, YouTube is the largest social media platform in the United States. In 2021, 81% of American adults reported using YouTube, compared to 69% who use Facebook and 23% who use Twitter. YouTube is the second-most visited domain on the internet, just behind Google, part of their parent company. Twenty-two percent of, or roughly 55 million, Americans, also report regularly getting news on YouTube. In addition, YouTube’s recommendation algorithm drives around 70% of total views on the platform . Taken together, these statistics suggest that YouTube’s recommendation algorithm is vitally important for news consumption. Understanding what content YouTube recommends to users—and the extent to which YouTube recommends various types of harmful content—is both an important and challenging problem to solve.

Why is studying YouTube’s recommendation algorithm difficult?

For starters, the recommendation algorithm is highly personalized, meaning one individual’s experience on YouTube can be completely different from another’s. While there are documented cases of individuals who are radicalized on YouTube, these cases don’t allow us to understand scale, prevalence, or cause, which is key to being able to remedy any adverse effects of the algorithm writ large. In addition, platform algorithms change frequently, and researchers outside the company do not have access to data to conduct audits.

For YouTube, outside researchers are limited to using user watch histories (donated by survey respondents to be used in research) or using web scraping to collect recommendations. Both methods present challenges for understanding the effects of the recommendation system on online consumption. The first, using donated watch histories, does not allow researchers to disentangle user demand for content from the supply the platform provides. That is, we can see what users choose to consume on YouTube. However, what users choose to consume within a YouTube session is a composite of what the platform chooses to show in recommendations, (i.e., the supply of videos) and the user’s choices of which videos to actually watch (i.e., user demand, or the user’s preference for particular content). 2 If researchers rely on watch histories and find increased consumption of right-wing content among a particular user, it could be a result of YouTube recommending increasing amounts of right-wing content to that user or it could be a result of that user receiving an ideologically diverse set of content but consistently choosing right-wing content. This is the crux of the difficulty in disentangling the effect of the algorithm from the effect of user choice on consumed content.

Alternatively, researchers can collect recommendations produced by an algorithm via web scraping. The way this works is that the researcher automates the process of visiting the YouTube webpage repeatedly and recording what is on it. In this way, the automated program can simulate the experiences of a user visiting YouTube. However, these automated visits to the web sites (we can think of this as a “bot” that watches YouTube videos) do not contain real user histories. No matter how a researcher programs a bot to simulate user behavior on YouTube, these user-agnostic recommendations disconnect the YouTube algorithm from the real user data on which it relies to operate, calling into question the extent to which web-scraped recommendations represent the lived experiences of users on the platform.

In our paper , we overcome the limitations of using watch histories or web scraping by analyzing what YouTube recommended to a sample of real users who participated in our study—that is, we are able to observe the actual videos that were recommended to users based on what YouTube chose to recommend to that person. However, instead of allowing users to choose which video to watch (and thus confounding the impact of the recommendation algorithm with user choice, as is the case when relying on watch histories) we constrain the user’s behavior for the duration of our survey by requiring our participants to click on a predetermined recommendation (i.e., always click on the third (or first, or fourth) recommendation). While we could have programmed a bot to do the same thing, we would not have been able to record what YouTube recommended to actual human users with real user histories. With the data collection method we employ, though, we can isolate the impact of the algorithm on which videos are shown to real users. When we combine this with a novel method to estimate the ideology of YouTube videos (described below) we are therefore able to assess the impact of YouTube’s recommendation algorithm on the presence of ideological echo chambers, rabbit holes, and ideological bias.

How We Did Our Research

In Fall 2020, we recruited 527 YouTube users and asked them to install a web browsing plug-in—a piece of software that would allow us to see what appeared in their web browser—to record their YouTube recommendations. 3 Each participant was randomly assigned one of 25 starting videos, consisting of a mix of political content across the ideological spectrum and some non-political content from music, gaming, and sports. 4 Users were then randomly assigned to one of five “traversal rules,” which instructed them to always click on a predetermined recommendation by the rank order of the recommendation. That is, a user would be instructed to always click on the first video, or always click on the second video, and so on. Respondents followed their assigned rule for 20 steps, and the browser extension collected the list of recommended videos presented at each traversal step. Thus, for each user, we would collect the set of twenty recommendations they received across twenty traversal steps, allowing us to understand the ideological content they were recommended.

After the survey, we used a novel method involving a machine-learning model (trained on Reddit and YouTube data) to estimate the ideology of each video recommended to users. To do so, we turn to Reddit, which is organized into sub-communities, or “subreddits,” for particular interests or beliefs. These subreddits cover a variety of topics, from broad forums like r/politics to discuss political content and r/music to discuss the newest music to more narrow forums like r/dataisbeautiful to show pretty data visualizations and r/backyardchickens to discuss chicken raising. For our method, we focus on subreddits dedicated to political content like the aforementioned r/politics and other subreddits such as r/The_Donald, r/liberal, or r/Conservative. The underlying assumption of our method is that videos shared in these political subreddits are likely on average to be ideologically aligned with that subreddit. For example, a conservative Fox News video would be more likely to appear in r/Conservative than in r/liberal. We can use this information—what videos appeared in what ideological subreddits—to estimate the ideology of YouTube videos.

To that end, we collect all posts with YouTube videos shared on 1,230 political subreddits from December 31, 2011 until June 21, 2021. We remove posts with negative “upvotes” (users can give a post either a thumbs up or a thumbs down, with a negative score indicating that the members of that subreddit did not like the content, which we interpret as an indicating that the content is not aligned with the ideology of the subreddit). We filter the remaining posts for basic popularity metrics to make sure the videos and subreddits being used to train our machine learning model are actually informative for the model. 5 With our final set of videos, we use correspondence analysis, a method commonly used in social sciences, to estimate the ideology of the videos shared on Reddit.

However, in our survey users encountered many videos that never appear on political subreddits. Therefore as a final step, we train a machine learning classifier to predict the ideology of YouTube videos using ideology estimations from the (Reddit-based) correspondence analysis. Using state of the art natural language processing, we trained a BERT model on video titles, tags, and descriptions to predict ideology of videos using the text features for the videos. Using this method, we can estimate ideology for all videos that users encountered in our survey. 6

What did we look for?

We use these ideology scores to measure the three concepts noted in the introduction of this article: ideological echo chambers , rabbit holes , and system-wide ideological bias .

Ideological echo chambers refer to a distribution of videos recommended to a user that is both ideologically homogeneous and centered on the user’s own ideology. For example, we consider a user to be in an ideological echo chamber if they are a conservative user who receives mostly conservative videos recommended from YouTube (and vice versa for liberal users). These users are in an “echo chamber” because they are only exposed to information that is consistent with their own ideology and prior beliefs. Echo chambers, as we define them in our article, are static: They represent the overall distribution of ideologies to which a user is exposed by the algorithm, rather than an evolving process that happens over the course of a traversal. So, if the YouTube recommendation algorithm puts users in echo chambers, we would expect users to see ideologically narrow content centered around their own ideology.

Alternatively, rabbit holes capture the process by which a user starts in a rich information environment and ends up in an ideologically extreme echo chamber. While much early social media research explored the prevalence of echo chambers on many platforms , rabbit holes are a specific phenomena related to personalized recommendation systems like YouTube’s. 7 The underlying intuition of this hypothesis is that recommendation systems provide a self-reinforcing feedback loop whereby users click on content that they like, and YouTube provides a more intense version of that content. In a non-political example, this could look like users watching videos about learning how to start jogging and then receiving recommendations for ultramarathon or triathlon-related videos. In the political context, a user might start on content about the presidential election and land on content espousing Holocaust denial or white supremacy.

Finally, we look at system-wide ideological bias , meaning bias in the overall recommendations of the majority of users. More specifically, system-wide ideological bias refers to the process by which all users—regardless of their own ideology—are pushed in a particular ideological direction. For example, if all users are pushed toward ideologically liberal content, we would consider YouTube’s recommendation system to have system-wide ideological bias toward liberal content.

What we found

The figure above summarizes our findings from the study and allows us to assess the prevalence—or lack thereof—of echo chambers, rabbit holes, and ideological bias. The figure displays the ideological distribution of recommended videos by traversal step—how many steps they had taken in the recommendation path–(0-5, 5-10, 10-15, 15-20) and a user’s self-reported ideology (conservatives = red, moderates = white, and liberals = blue) where a positive ideology score on the x-axis is more conservative and a negative ideology score is more liberal. Therefore, the distributions at the bottom of the figure illustrate the ideological distribution of video recommendations users received during the first five videos watched during the traversal task, while the distributions at top of the figure illustrate the video recommendations users received during the last five videos watched during the traversal task.

If the YouTube recommendation algorithm were fostering echo chambers, we would expect liberals’ recommendations to be more left leaning, conservatives’ recommendations to be more right-leaning, and little overlap between liberals and conservatives. If the recommendation algorithm was leading users down rabbit holes, we would expect the distribution of video recommendations to shift toward either extreme as the number of traversal steps increased (again, moving up the y-axis). Finally, if the recommendation algorithm has an ideological bias, we would expect the distribution of recommendations to shift uniformly across all users, regardless of their ideology, in one direction or another.

So what can we conclude from the figure above?

Echo chambers : We find that YouTube’s algorithm pushes real users into (very) mild ideological echo chambers. As we can see in the figure, by the end of the data collection task (the top part of the figure, traversals 15-20), liberals and conservatives received different distributions of recommendations from each other (we see that the three different color distributions in the top part of the figure do not perfectly overlap) in the direction we would expect: Liberals (blue) see slightly more liberal videos and conservatives (red) see slightly more conservative videos. The difference was statistically significant, but very small. Moreover, as the figure clearly illustrates, despite these small differences, there is a great deal of overlap between videos seen by conservatives and liberals at all stages of the traversal process. Furthermore, while the variance of these distributions does decrease across the different traversal steps, meaning that the ideological diversity of recommendations declines, it is only by a minimal amount.

Rabbit holes : While we do find evidence of the recommendation algorithm contributing to mild echo chambers, we did not find evidence that the algorithm led most users down extremist rabbit holes. There is little evidence from the figure that the ideological distribution becomes more narrow or increasingly extreme over time.

However, these conclusions are based on average outcomes. Does this mean that extremist rabbit holes don’t exist at all? To answer this question, we visually inspected each participant’s traversal path, looking for cases where the average ideology grew increasingly narrow (i.e., no liberal videos suggested at all) and increasingly extreme (i.e., more conservative than Fox News). We found that 14 out of 527 (~3%) of our users ended up in rabbit holes, which we defined as recommendations that are more liberal/conservative than 95% of all recommendations, and more narrow than a variance of 0.4 on our scale ranging from -2 to +2. While this is a substantively small proportion of users, due to YouTube’s size, a small proportion of users could still amount to non-trivial numbers of individuals occasionally falling into rabbit holes on YouTube. These findings underscore an important point about algorithmic systems and their effect on media consumption: Harmful effects are often concentrated among small numbers of users, and what is true for the platform as a whole can be very different for these sets of users.

Ideological bias : Finally, we found that, regardless of the ideology of the study participant, the algorithm pushes all users in a moderately conservative direction. If you look closely at the figure, you will see that all of the curves shift a bit to the right as they move up the y-axis. Although not large, these effects are statistically significant, and somewhat surprising given that we have never previously seen this feature of the recommendation algorithm publicly discussed. Moreover, the magnitude of the conservative ideological bias we identified far outweighs the magnitude of the echo chamber measure. This bias could be a result of two possible states of the world. First, YouTube’s library could consist of a normally distributed set of videos centered around moderate content, but the algorithm could choose to only recommend content that is skewed ideologically conservative. Second, the YouTube library could consist of content that skews conservative and the algorithm could recommend videos representative of that underlying distribution. Our study does not allow us to adjudicate between these two causes.

Contrary to popular concern, we do not find evidence that YouTube is leading many users down rabbit holes or into (significant) ideological echo chambers via its recommendation algorithm. While we do not find compelling evidence that these rabbit holes exist at scale, this does not mean that some that the experiences of the small number of individuals who encounter extremist content due to algorithmic recommendations are not consequential, nor does it mean that we shouldn’t be worried about the possibility for users to find harmful content online if they go searching for it. However, as we consider ways to make our online information ecosystem safer, it’s critical to understand the various facets of the problem.

While our study was designed to test whether the algorithm leads users down rabbit holes, into echo chambers, or in a particular ideological direction, these outcomes could still emerge from user choice (recall that the recommendations in our study were collected without user choice). So, for example, a well known article by Baskhy et al. shows that Facebook recommended an ideologically diverse array of content but users consistently clicked on ideologically congruent content. In another study of YouTube , Chen et al. found that other platform features—subscriptions and channel features—were the primary path by which users encountered anti-social content.

Furthermore, other platforms, like 4chan, are hotbeds for extremist content. Indeed, if an individual is bound and determined to jump down a rabbit hole online, they can do so fairly easily. What we explore in our work is incidental exposure: that is, users who are perusing content and encounter harmful content by accident, subsequently leading them down a rabbit hole. While recommendation systems may play a small role in this type of incidental exposure, we do not find significant evidence that they drive consumption of harmful content (at least on YouTube). Other studies have found that harmful content is often encountered off-platform via link sharing, driving users to extreme places on YouTube via the internet at large rather than the recommendation algorithm. That’s what makes this problem tricky. If it’s not just the recommendation engine and instead it’s the entire online ecosystem, then how do we fix it?

Our findings are consistent with—and add additional evidence to—a growing body of research showing that YouTube is not consistently pushing harmful or polarizing content to their users but rather that users self-select into viewing the content when offered. Collectively, the research suggests that there is unlikely to be one technological panacea to reducing the consumption of harmful content on YouTube. Instead, we need to be sure we focus both on the amount of harmful content online as well as the (many) paths which users might take to this content. Focusing solely on the role of YouTube’s algorithm in advertently luring people to extremist content may make for great headlines, but our research suggests that this alone is not going to get us at the crux of the problem.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Google, YouTube’s parent company, provides support to The Brookings Institution. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

• Although see Asimovic et al., 2021 for an example of a study that found evidence in Bosnia and Herzegovina that Facebook usage during a period of war remembrance actually reduced levels of ethnic polarization.
• Of course, another way that Google/YouTube can recommend YouTube videos is through Google Search results. Users can also find YouTube content linked to or embedded in other websites they visit. While this raises interesting questions for future research, our focus was on recommendations from within YouTube.
• This research was reviewed and approved by NYU Internal Review Board (#IRB-FY2020-4647).
• Starting videos were selected to be balanced across the ideological spectrum, including five liberal, five conservative, and five moderate starting videos. Additionally, we included nine non-political videos (three sports, three music, and three video gaming) in the starting video set. Respondents were randomly assigned one of these twenty-five videos at the start of the survey. Videos were refreshed throughout the study period to adjust for videos that were deleted or to add newer videos.
• For more details, see https://csmapnyu.org/research/echo-chambers-rabbit-holes-and-algorithmic-bias-how-youtube-recommends-content-to-real-users.
• For those interested in more information for how we used Reddit data to estimate the ideology of YouTube videos, including validation measures for the methods described in the text, see our working paper on “ Estimating the Ideology of Political YouTube Videos ”.
• This is not to say that other platforms do not have recommendation systems: Facebook and Twitter provide algorithmically-generated newsfeeds; Twitter provides following recommendations, TikTok’s experience is fully algorithmically generated.

Artificial Intelligence

Economic Studies

Center on Regulation and Markets

Artificial Intelligence and Emerging Technology Initiative

Julian Jacobs, Francesco Tasin, AJ Mannan

April 25, 2024

Elaine Kamarck, Darrell M. West

August 27, 2024

Jolynn Dellinger, Stephanie K. Pell

April 18, 2024

YouTube and the protocological control of platform organisations

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 24 January 2022

Issue publication date: 6 June 2022

This paper aims to examine the recommendation system of the video-sharing website YouTube to study how control of users is effected on online platforms.

Design/methodology/approach

This paper conceptualises algorithmic systems as protocols – technological and social infrastructures that both facilitate and govern interactions between autonomous actors (Galloway and Thacker, 2004, 2007). It adopts a netnographic approach (Kozinets, 2002) to study not only the formal, technological systems of the platform but also the systems as they were made sense of, understood and enacted upon by actors. It relies both on information as revealed by the organisation itself, as well as discussions between lay users in online forums and press coverage.

The results of this study indicate that the ways in which platforms selectively facilitate interactions between users constitute a form of control. While maintaining the appearance of an open and neutral marketplace, interactions on the platform are in fact highly structured. The system relies on the surveillance of user interactions to rapidly identify and propagate marketable contents, so as to maximise user “engagement” and ad revenue. The systems place few demands or restrictions on individual users, instead control is effected in a probabilistic fashion, over the population of users as a whole, so as to, in aggregate, accomplish organisational goal.

Originality/value

This paper contributes to the literature on accounting and control practices in online spaces, by extending the notion of control beyond overt rankings and evaluations, to the underlying technical and social infrastructures that facilitate and shape interactions.

• Neoliberalism
• Biopolitics

Xiang, Y. (2022), "YouTube and the protocological control of platform organisations", Qualitative Research in Accounting & Management , Vol. 19 No. 3, pp. 348-372. https://doi.org/10.1108/QRAM-04-2021-0060

Emerald Publishing Limited

Copyright © 2022, Yu Xiang.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Some of the largest and most profitable corporations in the world today have platforms at the core of their businesses, that is to say they own and operate technical and social infrastructures upon which actors may interact ( Srnicek, 2017 ). Whether they are social media services such as Facebook, marketplaces such as eBay, or companies in the “sharing economy”, they share a common characteristic – they all rely in their value creation process on the media contents, products, services and indeed the information and data, generated by external actors who may or may not have any direct economic relationship to the owners of the platform ( Srnicek, 2017 ). While the companies owning and operating the platforms are quite invariably fairly conventional and well-defined hierarchical organisations, the platforms themselves are often rather different ( Kornberger et al. , 2017 ). A significant part of the value creation process is externalised to users outside of the formal organisation; meanwhile, there is a degree of “openness” in terms of access to platforms, i.e. there is usually little-to-no vetting of prospective users before they are admitted to the platforms; moreover, an often significant portion of the userbase do not have any direct economic relationship with the platforms or their owners (e.g. no money changes hands between YouTube and an ordinary viewer on the site); nor are most users legally bound to the platforms beyond a terms of service agreement ( Bhimani and Willcocks, 2014 ; Kornberger et al. , 2017 ; Srnicek, 2017 ). In other words, what we have before us is a form of organisation that challenges our conventional understanding of control as a phenomenon within well-defined organisational spaces.

As Srnicek (2017) alludes, the fact that platforms are ostensibly “open” in this manner does not render the concept of control irrelevant. Indeed, as profit-driven businesses, it is very much in the owners’ interest to control the nature of interactions on platforms ( Kornberger et al. , 2017 ; Srnicek, 2017 ). The relevant question in this context is how control is enacted on platforms, as well as the implications this has on our understanding of control as social technology in general. Using the case of YouTube, I will henceforth argue that the platforms’ systems of technical and social infrastructures effect a novel form of control, one that does not so much discipline individuals in enclosed organisational spaces, but rather facilitates interactions differentially and directs the flow of resources and people, and in so doing achieves organisational goals in a probabilistic fashion over a population as a whole.

Platforms, control and biopolitics

There is now a not-insignificant body of research on the issues of control and various forms of information technology, which have highlighted some of the issues associated with these types of organisations. For example, within organisations, decentralised information systems can (often unintentionally) create multiple centres of calculation, lateral networks of surveillance and rhizomatical controls, irrespective of formal organisational hierarchies ( Brivot and Gendron, 2011 ; Quattrone, 2016 ; Quattrone and Hopper, 2005 ). The notion of control as enclosed within organisational boundaries is also eroding. Beyond the enclosure of organisations, social media and crowdsourced review and ratings sites are increasingly providing channels through which voices from outside of organisations are enabled to enact something akin to disciplinary power, as actors, with or without formal association, generate a plurality of viewpoints that may have material impact on the operations of the organisation ( Agostino and Sidorova, 2017 ; Arnaboldi et al. , 2017a , 2017b ; Arnaboldi et al. , 2017a , 2017b ; Brivot et al. , 2017 ; Jeacle and Carter, 2011 ; Scott and Orlikowski, 2012 ; Viale et al. , 2017 ). In other words, the binary distinction between the centre and the periphery is blurred with the creation with multiple, intertwined centres of calculation ( Agostino and Sidorova, 2017 ; Arnaboldi et al. , 2017a , 2017b ; Brivot and Gendron, 2011 ; Kornberger et al. , 2017 ).

At the same time, the individual is increasingly subject to intersecting systems of control ( Deleuze, 1992 ; Martinez, 2011 ). Businesses come to increasingly rely on the constant, technology-enabled surveillance of the individual as a source of data and insight ( Agostino and Sidorova, 2017 ; Bhimani and Willcocks, 2014 ; Brivot et al. , 2017 ; Fourcade and Healy, 2013 ; Viale et al. , 2017 ). Closely related to this and the aforementioned erosion of organisational boundaries, the categories of individuals being subjected to surveillance and control have also been expanded, whereas earlier efforts have focused on actors with whom the firm has had some form of transaction, e.g. subcontractors, employees or customers, there is now a movement towards any and all users of platforms ( Agostino and Sidorova, 2017 ; Bhimani and Willcocks, 2014 ; Brivot et al. , 2017 ).

Thus, whereas more traditional conceptualisations of control envision enclosed, calculable organisational spaces within which individuals are subject to assessment and comparison by hierarchical, panoptic surveillance, according to some stable set of standards, platforms present the possibility of organisations without clear boundaries, without clearly defined roles for actors, and with multiple centres calculations. Yet this does not eliminate the need for control. To the contrary, far from being neutral marketplaces, platforms are deeply shaped by the intentions and goals of the platform owners ( Galloway and Thacker , 2004, 2007 ; Kornberger et al. , 2017 ; Srnicek, 2017 ). Consequently, the pertinent question is rather how control is enacted on platform, and what this implies for our understanding of control as a social practice.

Of course, accounting as a discipline is no stranger to the notion of control outside of the confines of hierarchical institutions. As Munro (2012) and Cooper (2015) made clear in their reviews of the use Foucauldian theories in management and management accounting studies, respectively, much of the theoretical inspiration for the notion of control as predominantly occurring within enclosures came from Foucault’ writings on the use of disciplinary power in early-modern institutions ( Hoskin and Macve, 1986 ; Munro, 2012 ; Cooper, 2015 ). Yet towards the end of his life, Foucault was also keenly aware that new systems of power were emerging, not wholly replacing the old, but increasingly existing beside and transforming the ways older forms of power are exercised. Much of Foucault’s later writings on biopolitics were concerned with systems of power that operate not within enclosed spaces and on individual bodies, but rather seek to organise the circulation of resources and people. Through technologies such as statistics, a new subject is constituted, that of the population as a whole – a quasi-biological superorganism, comprised large number of interacting individuals ( Foucault, 2008 ; Munro, 2012 ; Cooper, 2015 ).

Foucault links the rise of modern biopolitics with the ascendancy of neoliberalism as a system of normative political-economic reasoning. To Foucault, two key elements differentiate between early-modern liberalism and modern neoliberalism – that of the market, and those individuals that operate within them. Whereas liberal thinkers such as Adam Smith saw the market as naturally occurring, naturally self-regulating, and over time would tend to produce the greatest collective benefit if only left alone, Foucault (2008) argues that neoliberal theorists tend to view the market as requiring continuous and covert interventions to promote competition and entrepreneurship, so that it may operate as if it is naturally self-regulating ( Munro, 2012 ; Cooper, 2015 ). Moreover, whereas individuals were previously conceived merely as parties in exchange relationships, under neoliberalism individuals are re-imagined as entrepreneurs of the self, active economic subjects constantly seeking to maximise the returns on their human capital , that is to say their innate or acquired abilities ( Foucault, 2008 ; Munro, 2012 ; Cooper, 2015 ). Instead of confining bodies and subjecting them to discipline, entrepreneurial subjects “are being encouraged to circulate, to exchange and, most importantly, to compete” ( Munro, 2012 , p. 353).

The exercise of biopolitical power then, lies in part in intervening in the market environment so that entrepreneurial individuals acting in self-interest would tend to, in aggregate, make decisions in ways that are socially and economically desirable, and in part in instilling and normalising an entrepreneurial spirit in the individuals, so that not only would they seek to maximise their own economic interests but also respond to modifications to the market environment in a desirable manner ( Foucault, 2008 ; Munro, 2012 ; Cooper, 2015 ). And herein lies also the inherent contradiction of neoliberal biopolitics – while the individual is cast as the empowered entrepreneur, free to pursue their own self-interest, ultimately they must do so under market-like contexts not of their own choosing, and whose design may embody interests incongruent to their own ( Cooper, 2015 ). Entrepreneurial subjects are encouraged to circulate, to exchange and to compete, but the manners in which such circulation, exchange or competition can occur are subject to continuous interventions.

While the rise of neoliberal ideology predates today’s internet-based platforms, as Van den Bussche and Dambrin (2020) contend, these organisations nevertheless exemplify neoliberalism in the post-industrial age. In particular, in their reliance on user-generated contents, products and services, platforms provide the infrastructure through which personal properties and abilities become assetised and monetisable ( Van den Bussche and Dambrin, 2020 ). The individual user is recast as an active market participant, empowered to maximise the return on their assets, whether tangible or intangible, innate or acquired. Moreover, as Arvidsson (2005) and Srnicek (2017) note, platforms users contribute to the value creation process not only through material labour, but increasingly through immaterial labour as well – not only do users provide media contents, products and services, they are also increasingly being relied upon to produce trust, affects, cooperation and a sense of community and shared meaning, all of which can become sources of value for platform owners. Indeed, platforms such as TripAdvisor unambiguously market themselves not only as services but also as communities of like-minded individuals, inherently more trustworthy than distant experts ( Jeacle and Carter, 2011 ; Scott and Orlikowski, 2012 ).

While few accounting studies on platforms have drawn explicitly from biopolitics ( Van den Bussche and Dambrin, 2020 , notwithstanding), a number of works have highlighted the complex nature of neoliberal subjectivity on platforms. Across a number of platforms, instead of anonymous providers of goods and services, users are encouraged to distinguish themselves as unique, identifiable individuals. Thus for example, on TripAdvisor ( Jeacle and Carter, 2011 ; Scott and Orlikowski, 2012 ) and IMDb ( Bialecki et al. , 2017 ), users can create highly detailed personal profiles and leave lengthy written reviews; similarly, users on TripAdvisor, eBay ( Kornberger et al. , 2017 ), and Amazon ( Jeacle, 2017 ) can earn various “badges” from the platform and fellow users. Here, the notion of the user as an entrepreneur of the self is closely linked to immaterial labour – the sense of trust and community is contingent upon the presence of unique, identifiable individuals on the platform, who are as much a part of the value proposition of the platforms as the goods or services themselves; users are drawn to these platforms not merely because they offer the possibility of transactions, but because these transactions are between ostensibly unique, identifiable and relatable individuals, as opposed to faceless businesses ( Jeacle and Carter, 2011 ; McDaid et al. , 2019 ; Van den Bussche and Dambrin, 2020 ).

This also serves to highlight a natural point of intersection between biopolitics and the study of management control as a phenomenon, namely, that of the practice of mutual evaluation between platform users. On open peer-to-peer platforms such as Airbnb and eBay, systems of evaluation are vital in transforming uncertain and unknowable online spaces populated with distant actors into (what appears to be) functioning marketplaces and safe communities ( Kornberger et al. , 2017 ; McDaid et al. , 2019 ; Van den Bussche and Dambrin, 2020 ). Part and parcel of the constitution of the entrepreneurial subject is the mobilisation of accounting devices such as reviews and ratings to visibly assign values to the users’ human capital ( Van den Bussche and Dambrin, 2020 ). Mutual evaluation is even present on platforms where users do not provide products or services to each other. In Jeacle and Carter’s (2011) case for example, while the objects of evaluation on the platform TripAdvisor are ostensibly hotels and restaurants, users nevertheless engage in mutual evaluation, so as to form a picture of the relative trustworthiness of each other, and by extension, each other’s judgement. Similarly, Bialecki et al. (2017) find that reading user-generated reviews on the film review site IMDb often involves reading the profile of the authors, to gauge whether the authors’ interests and tastes align with that of the reader. Indeed, as Jeacle’s (2017) study of Amazon reviews illustrates, even user-generated evaluations themselves can become the object of evaluation, with users leaving rating for each other’s product reviews.

This in turn has consequences for interactions on these platforms. For example, McDaid et al. (2019) and Van den Bussche and Dambrin (2020) find that reviews on Airbnb overwhelmingly trend towards the positive, which the authors argue is a consequence of the reciprocal nature of reviews on the platform – accommodation providers and guests on the platform leave reviews for each other, and as these evaluations are public and closely linked to identifiable individuals, as opposed to faceless organisations, there is a tendency among users to avoid conflict and confrontation. The market thusly constituted is at once dysfunctional, in that it is difficult for users to judge the quality of the services based on the review scores alone, yet al.so ultimately in the interest of the platform owners, in that the marketplace appears safe and friendly. ( McDaid et al. , 2019 ; Van den Bussche and Dambrin, 2020 ).

The cases of McDaid et al. (2019) and Van den Bussche and Dambrin (2020) are suggestive – the nature of user interactions on Airbnb is inextricably linked to the peer-to-peer character of its marketplace. Yet the marketplace of AirBnB is neither naturally occurring nor neutral. Rather, the rules of interactions (mutual and public) in the marketplace embody the politics and interests of the platform owners ( Srnicek, 2017 ). And in as much as the nature of the marketplace is shaped with the accomplishment of organisational goals in mind, it ought to be viewed as a form of control ( Van den Bussche and Dambrin, 2020 ). From this perspective, it becomes apparent that while previous studies in control on platforms have emphasised the constitution of entrepreneurial subjects through evaluations, relatively little attention has been directed at the exercise of control through interventions in the marketplace in areas other than those of evaluative mechanisms. Evaluations are but one facet of the marketplace of platforms, and it seems at least plausible that other mechanisms are also at work in shaping the actions of users. Indeed, theoretical works such as those by Kornberger et al. (2017) and Srnicek (2017) propose that control on platforms goes beyond distributed and crowdsourced evaluations; rather, platforms, through their myriad underlying mechanisms exert control over users by selectively creating relationships between users; in Kornberger et al. (2017) ’s words, it “is not the clear and parsimonious one of ranking and ratings, but rather a complex set of possibilities for making connections” (p. 87).

In summary then, whereas earlier works on platforms have focused on rankings and evaluations, Foucault’s concept of biopolitics suggest that other mechanisms of control are at work – the control of populations as a whole is not merely a question of fostering entrepreneurial subjects through the disciplinary power of evaluations but also how such subjects are made to circulate and are brought together. This is to say that while overt evaluations on platforms must not be overlooked, as far as control is concerned analytical attention must also be directed at the wider range of devices that connect users to one another. The ways in which users are connected to goods, services, media contents, and ultimately each other, are not incidental, but rather a matter of design. While this theme of circulations and interactions has been elaborated upon in theoretical works such as Kornberger et al. (2017) and Srnicek (2017) , it remains empirically under-explored. This paper addresses this lacuna, by asking, beyond evaluations, how is the control of users effected on platforms ? And it does so by examining the video-sharing platform YouTube and its systems, through the theoretical lenses of Galloway and Thacker’s (2004) notion of the protocol – systems of technical and social infrastructures that both facilitate and govern interactions, and highlight the ways in which such systems effect rigid yet decentralised control on platforms.

Theoretical framework

Galloway and Thacker (2004 , 2007 ) coined the term “protocol” to refer to the inseparability between all types of technical and social constructs that facilitate interactions between interconnected-but-autonomous actors, and the ways in which these constructs shape interactions. Just as the person of the sovereign and sovereignty as a form of power, or disciplinary technologies and disciplinary power, are mutually constitutive and inseparable, so are the means of interactions and the logic through which interactions are governed ( Galloway and Thacker, 2007 ). This conceptual congruence is not incidental, as Galloway and Thacker (2007) view protocol as a development of Foucault’s notion of biopolitics. Protocol shares with biopolitics a concern for the operation of power over the population as a statistical whole, as well as the ways in which such a body is constituted and made amenable to intervention through the production of various forms of numerical information.

With this as the starting point, Galloway and Thacker (2004 , 2007 ) argue that in modern, networked settings, protocol is one way to understand the constitution of such biopolitical bodies and the means through which interventions are made possible. While biopolitics does not supplant discipline entirely, and the two modes of power operate side-by-side, under biopolitics the subject of control is no longer the individual body but the population as a whole, ( Foucault, 2008 ; Munro, 2012 ). As Deleuze (1992) argues, human actors are increasingly divided, surveilled, reduced to quantifiable measures, and aggregated to produce the “population”; and this in turn produces a new form of subject, the “dividual”, who does not exist within the system as a distinct individual, but solely as a particular set of generalisations derived from the population as a whole. The ontology-epistemology relation between the individual and the population becomes uncertain – on the one hand the population is a statistical aggregation of numerous individuals, yet on the other the individual becomes only knowable as a set of statistically derived traits.

Thus, for example, the individual becomes an “under-performer (in relation to the population”, “heavy user (in relation to the population)”, or “early adopter (in relation to the population)”. This, Galloway and Thacker (2007) argue, is the central premise of the notion of protocols – that under a protocological system of control, there is no meaningful difference between life and life-as-information, and that this informatics-based perspective on life enables systems to effect rigid control in open systems, without a central authority; they do so by facilitating interactions differentially, not between identifiable individuals as such, but between “individuals”, that is to say, in terms of reducible and quantifiable traces left by the individuals.

To illustrate this, Galloway and Thacker (2007) use the metaphor of biology. In a strictly physical sense, the cell is a liquid-filled space enclosed by a permeable lipid layer. Yet, cellular biology can be conceptualised as a protocological system, in that even though their motions within the intra-cellular fluid are random, the interactions between cellular components are governed by some basic and universal principles. Any number of molecules can and will enter into the cell, come into contact and interact with one another, but only some of these interactions are thermodynamically favoured and form stable bonds. Errors are not physically impossible (and are in fact fundamental for mutations and ultimately, the process of evolution), but are infrequent because they are thermodynamically unfavourable.

What this metaphor illustrates are some of the features of the concept of protocol. First, protocols as systems of control are inextricably linked to an informational view on life, in a manner analogous to the Foucauldian notion of power-knowledge. The regulation of cellular functions cannot be understood fully except as expressions of the molecular genetics of the DNA (and more fundamentally, thermodynamics/statistical mechanics), that is to say the cell as a system of information as encoded in the DNA. Just as biopolitical power and statistical knowledge are inseparable and mutually constitutive, so too is the protocol and life-as-information – the protocological system of control is fundamentally reliant on the reduction of life to its calculable and combinable digital traces. Second, protocols are relational, in that they are manifest only in the ways in which actors interact. Any number of interactions between molecules is possible, but some are statistically more favoured. Protocols govern interactions by facilitating them differentially, and their effects are probabilistic. In the cellular metaphor, the laws of genetics or physics are not “things” separate from the objects which they govern, but are made manifest in cellular activities. Along this vein, the protocol cannot be reduced to the technical and social constructs that facilitate interactions, but rather describes the ways in which these constructs are embedded, embodied and enacted by actors in encounters with each other. Third, what this regulation of interactions accomplishes is rigid control, not through a central authority, but distributed agencies. In the strictest sense, as mere collections of molecules, the cellular components obviously have no agencies of their own – their motions are entirely random, they do not “know” what to do or where to go. But they can also be said to possess a form of distributed material agency – in that the laws of physics are not enforced by any central cellular authority, but are embodied in the ways in which individual atoms within molecules interact with one another; likewise, genetic expressions are not governed by any central biological authority, but are made possible by the genetic material, i.e. the DNA, within every cell of the body as a whole. This distributed form of control is effected by systems of protocols, acting in conjunction with each other.

Of course, human actors are not cells, nor do they operate (solely) through laws of physics. To the extent that human actors interact through social and technical constructs, the concept of protocol denotes all of these apparatuses and the ways in which they facilitate and shape interactions ( Galloway and Thacker, 2004 , 2007 ). Thus, to use another example, interactions between actors on the internet is facilitated and governed by a host of technical (e.g. Telnet, TCP, IP) and social (e.g. laws, rules and etiquettes) protocols. These can be regarded as systems of protocols because they simultaneously make interactions possible and impose a certain kind of logic to said interactions. Thus, video-sharing/streaming sites would not be possible without technical systems such as DASH, an agreed-upon set of technological standards that enables the delivery of audio-visual content over the internet, or social systems such as copyright laws or social mores. Moreover, in line with the informational view, these are not interactions between individuals per se , but rather the flow of digitalised information traces generated by individuals as they interact with systems, and it is precisely their informational nature that renders them amenable to transfer and governance. It follows that the concept of protocol can be understood as the epistemological construct that denotes the practices through which subjects are governed in networks, at once reliant upon and yet irreducible to technological and social infrastructures. In other words, the “protocol is a technology that regulates flow, directs [space], codes relationships, and connects life forms” ( Galloway and Thacker, 2004 , p. 10).

To Galloway and Thacker (2007) , systems of protocol are in constant states of dynamic tension. For one, the ontological statuses of the individuals and the network can never be fully resolved. Protocol as a form of control acts upon the network as a whole. In one sense, the network is composed of a number of autonomous but interconnected individuals, and thus the individual precedes the network. But on the other hand, as far as the network is concerned, the individual only exists within the network in terms of their interactions as mediated by the system of protocols (i.e. one is not connected to a computer network merely by the act of physically connecting a network cable, rather presence on the network is contingent on sending and receiving data through the network). They who are present are the Deleuzian “dividual”, the digitalised, combinable traces left by individual networked actors; one is at once a “unique visitor” as well as part of an overall “network traffic” ( Galloway and Thacker, 2007 ).

Furthermore, the central role played by technical and social constructs highlights a tension between two forms of control, that which is within the network and that which is “over” the network. Control within the network itself is decentralised – for example, network protocols on the Internet are not enforced by a central authority, but are rather embedded in the software and hardware configuration of individual networked computers. Yet, at the same time, the technical and social infrastructures upon which protocols rely are not laws of nature, but rather human creations that embody the politics of their creators. The distributed agencies of networked individuals and the materialised agencies of the owners of the infrastructures are thus in a constant state of negotiation ( Galloway and Thacker, 2007 ). While the infrastructures of network often appear as opaque, inaccessible and non-negotiable, seemingly imposed by the owners of the platforms, the protocological systems that emerge from them are ofttimes more indefinite in character – the protocol is always accompanied by the counterprotocol ( Galloway and Thacker, 2007 ). The protocological system relies on an informational view of life as quantifiable and digitalisable traces, yet it does not always succeed in imposing this epistemology universally. Nor is there one singular, sanctioned mode of interaction, but multiples, as actors within the network find shortcuts, loopholes, workarounds and exploits ( Galloway and Thacker, 2007 ). To Galloway and Thacker (2007) , counterprotocols represent resistance within networks, resistance to the ways in which life is co-opted by power through quantification; those who engage in counterprotocols do not seek to efface the networks, but to find other ways of being and interacting within them.

In relation to the research question behind this paper, namely, that how the control of users are effected on platforms, this paper contends that platforms such as YouTube can be viewed as a system of protocological controls consisting of overlapping layers of infrastructure, some of which technical, others social, all emergent and subject to negotiation, which together governs the interactions on the platform. In the analysis that follows, I will describe the various systems on YouTube in terms of the technical and social infrastructures which facilitate and govern interactions on the platform and demonstrate how these systems work together to effect a decentralised, protocological form of control, one that shifts its focus away from the evaluation of users as individuals, and instead aggregates and processes digital traces left by platform users and in so doing achieves organisational goals.

Research methods

As is common amongst platform organisations, YouTube is generally reluctant to speak of its systems in detail, which poses considerable challenge to prospective researchers. Earlier studies in YouTube have often relied on the platform’s open-access Data API to quantitatively reverse engineer its algorithms, ( Bishop, 2018 ; Smith et al. , 2018 ; and van Kessel et al. , 2019 ; but also “lay” researchers outside of academia, such as Gielen and Rosen, 2016 ; Sybreed and Sealow (pseudonyms), 2021 ). While such an approach has provided valuable insights into the inner workings of the platform, as Bishop (2018) points out, an unavoidable shortcoming of relying on YouTube’s own interface is that the underlying algorithms are constantly revised and experimented upon by YouTube itself, and any data thus gathered can only be considered provisory. Moreover, as protocols, YouTube’s systems are as much social as they are technical, and the social infrastructures of the site are a collective accomplishment, not only of YouTube and its formal organisation but also of all of its users, through their interactions with the platform and each other ( Galloway and Thacker , 2004, 2007 ). Indeed, in this sense control is not solely the purview of the controller, but relies also on the intentions and actions of the controlled, and contingent upon the ways in which they are understood by the rank-and-file users ( Ahrens and Mollona, 2007 ). The opacity of the algorithms, the piecemeal and equivocal ways in which they are revealed by the platform, and the ways in which users make sense of and navigate around the formal systems are all integral parts of YouTube’s system of protocols, as much as any part of the technical infrastructure. Furthermore, as alluded to earlier, though contingent on formal, technological infrastructures of the platform, the protocological system is irreducible to formal systems; the system is only protocological when it is “live” – when they are embodied and enacted by networked actors in their interactions with each other ( Galloway and Thacker, 2007 ). This is to say that, while not disregarding the formal, technical systems, to study YouTube as protocological system of control, one need to go beyond the formal and examine the experience of the lay users of the platform.

From this perspective then, it becomes imperative to examine not only the technical infrastructures of YouTube but also the wider system around it – the ways in which it is presented to users, talked about among users and the public at large, and the manner in which it is understood, worked around, and allowed to influence actions. Thus, going forward, this paper will adopt a “netnographical” approach, that is to say one that adapts ethnographic techniques to study computer-mediated communications within internet-based communities ( Kozinets, 2002 ). The empirical focus is not only on the technical infrastructures as such but also the ways in which they are communicated to the users of the platform, and how such communications are understood and talked about by users. The goal is to understand YouTube not only in terms of a formal system of protocols but also more crucially how such a system shapes interactions with and on the platform in practice.

The data collection process is as follows. First, I familiarised myself with the general functionalities of YouTube as a platform, both from the perspective of the user as viewer and as content creator [ 1 ]. Second, I looked to the information made available by YouTube regarding its systems and algorithms. In this regard, Covington et al. (2016) constitute a crucial first-hand source of the inner workings of YouTube, as the authors were engineers at YouTube. Furthermore, material was also gathered from the official support page for YouTube [ 2 ], YouTube’s own tutorials for content creators [ 3 ], the organisation’s official blog [ 4 ] and the various official YouTube channels operated by the platform itself. The importance of these lies not only in their prima facie value as first-hand descriptions of the systems but also represents the ways in which the inner workings of the platform are communicated to platform users, a form of social action important in its own right ( Kozinets, 2002 ).

Third, and in line with the recognition that protocols are social accomplishments as well as technical ones, I have conducted observations on commentaries left by users of the platform, both on YouTube itself in response to various videos, official and unofficial, touching on YouTube’s systems of protocols, as well as posts on a number of sub-forums (so called “subreddits”) particularly popular with YouTube users on the internet forum platform Reddit [ 5 ]. The choice of these specific research sites is to a large extent influenced by Kozinets’ (2002) exhortations in terms of the selection of online communities – in general, it behoves the researcher to direct attention to online communities that have a focus that is closely related to the research topic, have relatively high traffic and numbers of discrete users, and consequently large number of social interactions between users.

Fourth, I have also examined the press coverage of YouTube and its algorithms, both in mainstream press, as well as more specialised, trade media. These not only form an important source of secondary data on the platform itself, but are also important resources for platform users, and are thus often the subject of discussions among users. In this sense, they can be viewed as an integral part the “YouTube community”. Lastly, I looked to existing academic literature on YouTube in the field of new media studies, a discipline more accustomed to the exploration of media in the internet-era. In general, the empirical focus is on information made available after 2016, as this particular year marks the point in time at which YouTube transitioned toward deep learning technology based on artificial neural networks, and much of the underlying algorithms of the platform were overhauled in the process ( Covington et al. , 2016 ). All of the netnographic data were bookmarked and saved as screenshots, one for each web page. All in all, a total of more than 250 pages were saved in this manner.

Drawing on this body of netnographic data and informed by the relationality of protocological systems, the first step in the analytical process focused on identifying technical and social infrastructures that facilitate interactions between platform users, whether as content creators, viewers or advertisers. As can be expected for an online platform, these interactions are not directly between users per se , but are rather in the form of digitalised information and mediated by the platform. The focus on interaction highlighted the commonality in a range of seemingly disparate devices and systems. In this light, mechanisms such as the search function, advertisements, comments, the subscription/notification function and livestream chats all serve the same overall purpose – to connect users to each other via user-generated digital contents, be they in the form of videos, advertisement, comments or chat messages. From this starting point, discourses in internet communities such as Reddit as well as in press coverages were analysed to place the systems in their proper context, not as mere descriptions, but as they are made-sense-of, understood, worked around, and otherwise enacted by users in their everyday interactions with the platform. In other words, what emerged was the protocological system in action – a network of independent actors whose interactions are at once facilitated and constrained by the formal systems. Thus, for example, it became apparent that for smaller creators, the major challenge is managing one’s own visibility and accessibility within YouTube’s Search and Discovery system, and far from being a neutral search function, the Search and Discovery system constitutes a form of control that governs content by selectively enabling interactions.

YouTube as technical infrastructures

YouTube is the world’s most popular video-sharing service and the second most visited website on the Internet. At the most basic level, it provides hosting and streaming for user-created video contents and generates revenue through advertisement. Users may “like” and “dislike” videos, leave comments, subscribe to channels, create playlists, share videos on social media platforms and report videos for inappropriate or abusive content. It can be analysed as a system of protocols because fundamentally it facilitates interactions between users-as-content-creators, users-as-viewers, as well as advertisers, and it does so in a highly structured manner, chiefly through two systems of automated algorithms, officially referred to as “Search and Discovery” [ 6 ] and “Monetisation” [ 7 ].

Search and Discovery is the general term used by YouTube to refer to all facets of the platform relating to its search and recommendations functions. It presents search results when users enter queries into the search bar at the top of the page; it makes personalised video recommendations on the front page, on the pages of individual videos, and at the end of videos; and it queues the next video to be played if the “Autoplay” feature is enabled. YouTube describes the function of Search and Discovery as twofold: “[to] help viewers find the videos they want to watch, and [to] maximize long-term viewer engagement and satisfaction” [ 8 ]. The most detailed and up-to-date account of the Search and Discovery system comes from Covington et al. (2016) . In the paper, the authors note that as of 2016 the YouTube Search and Discovery system comprises two separate artificial neural networks, candidate generation and ranking ( Covington et al. , 2016 ). During the candidate generation phase, a user query (for example landing on the YouTube front page or a search query using the search bar) is processed together with the user’s search history, watch history, feedbacks (“likes”, “dislikes”, etc.), demographics (age, gender, etc.), geographic location and other personal contextual information to generate a list of “hundreds” (p. 2) of candidates from a video corpus of “millions” (p. 2) ( Covington et al. , 2016 ). The candidate generation algorithm also takes into account the “age” of the videos, i.e. the time since the videos were uploaded ( Covington et al. , 2016 ).

During the subsequent ranking phase, videos in the candidate list are winnowed further to “dozens” based on user data, as was done in the candidate generation phase, as well as “hundreds” (p. 5) of criteria for each and every video in the candidate list, including channel and video watch times, thumbnails, languages, tags, descriptions, automated closed captioning texts and other content characteristic ( Covington et al. , 2016 ). At the end of the ranking phase, the final results are presented as a hierarchical list of videos to the user, with those videos thought most likely to generate “engagement” placed at the top. The candidate generation and ranking algorithms are continuously refined in real time, using live A/B experiments – the algorithms record and measure user “engagement” in terms of “click-through”(i.e. the videos actually selected and viewed by the user), as well as the watch time for each selected result ( Covington et al. , 2016 ). Furthermore, Covington et al. (2016) note that although various explicit feedback mechanisms exist on YouTube (as best exemplified by the “likes”/“dislike” buttons), these are outnumbered by several orders of magnitude by implicit feedbacks that do not explicitly ask the user to rate or assess the videos (as exemplified by the metrics of watch time and watched-to-completion), and that YouTube as a platform is increasingly relying on implicit feedbacks, as these appear to reveal more about user preference than explicit feedbacks.

YouTube’s main source of revenue is advertisement, and this is the domain of the Monetisation system. To advertisers, YouTube offers skippable and un-skippable video ads placed before, during, and after eligible videos, masthead ads on its front page, as well ads appearing next to related videos and search results [ 9 ]. The system is broadly based on parent company Google’s AdSense technology. Eligible content creators are paid a share of the ad revenue through the so-called YouTube Partner Program [ 10 ] [ 11 ]. Advertisements can selectively target specific audience groups based on geographic regions, demographics (age, gender, marital and parental status, interests, etc.); individual user data such as operating system, browser types, browsing and search history can also be used to select for suitable ads [ 12 ]. Ads can also be placed to target particular types contents – from specific videos or YouTube channels, to videos on specific topics, or based on criteria such as watch times, thumbnails, languages, tags, descriptions, automated closed captioning texts and other content characteristics of particular videos [ 13 ] [ 14 ].

Besides the two large systems of Search and Discovery and Monetisation, operate a whole host of smaller ancillary systems and devices. Thus, on any given page for a particular video, one finds a row of four buttons beneath the lower right-corner of the video window – “like”, “dislike”, “share”, and “save”. Users can show approval or disapproval for a particular video by interacting with the “like” and “dislike” buttons, beside which the total number of likes and dislikes are displayed. To the right of these is the “share” button which, as the name implies, enables the video to be shared on social media platforms and messaging services. Further to the right is the “save” button, which allows the user to add the video to their own customised playlists. Further down the page is the red “subscribe” button, which allows the user to subscribe to the channel of the video creator. Once subscribed, the channel becomes directly accessible as a link on the user’s front page, and the user is then able to choose to receive notifications when new videos are uploaded to the channel.

Users are also able to interact with creators and other users somewhat more directly via comments and chats. Unless disabled by the video creator, viewers and content creators alike can leave comments to videos and reply to other’s comments, and the comments themselves can be liked or disliked. Moreover, the order in which the comments are displayed is structured – comments written by or replied to by the original video creator are always displayed at the very top, other comments are then listed according to some combination of numbers of replies and likes, and the age of the comment, with newer and more popular comments displayed higher up on the page. Furthermore, during live broadcasts (“livestreams”), viewers are also able to type messages direct into a chat window, which automatically scrolls and displays messages in chronological order. Beginning in early 2017, a Super Chat feature was also added – viewers of the live broadcasts can pay to have their chat messages pinned to the top of chat window; the revenue from the Super Chat is then shared between the creator of the broadcast and YouTube [ 15 ].

Together, Search and Discovery, Monetisation, and the various ancillary systems form the algorithmic infrastructure of YouTube, one that regulates flows of net traffic and creates spaces for interactions between creators, viewers and advertisers, not directly, but mediated through digital contents in the form videos, comments, chat messages and ads. And it does so through algorithmic surveillance of the behaviours of users, whether as viewers or content creators, and reduces said behaviours into combinable and calculable digital traces ( Deleuze, 1992 ; Galloway and Thacker , 2004, 2007 ; Martinez, 2011 ). As Covington et al. (2016) reveal, the systems do not facilitate interactions neutrally, but rather seek to maximise viewer “engagement”. YouTube representatives have at various times claimed that, above all, the algorithms prioritise watch time, that is to say the length of time spent watching a particular video, and that total watch time has been a key performance metric for the company ( Goodrow, 2017 ; Meyerson, 2012 ). Thus, Search and Discovery constantly seeks to learn user preferences and presents results most likely to maximise watch time for each particular user, and is continuously refined through live tests. At the same time, newer contents are constantly introduced into the results, so that their characteristics relative to viewer preferences can be identified and categorised. The end result is that as the user engages with the platform, the systems progressively present the user with longer and more popular videos ( Smith et al. , 2018 )

YouTube as social infrastructures

YouTube as a system of protocols is both technical and social in nature, and while the Search and Discovery and Monetisation systems certainly play a critical role, the platform’s functioning is contingent on a degree of cooperation from users, not the least because it depends entirely on user-generated contents. Thus, on top of the technical protocols of Search and Discovery and Monetisation, YouTube uses a host of services that selectively disclose the workings of the technical systems. First, YouTube has separate guidelines for acceptable behaviours on the platform – “Community Guidelines” apply to all users on the platform, whether as content creators, viewers, or advertisers [ 16 ]; whereas “Advertiser-friendly content guidelines” govern the types of content that can be monetised [ 17 ]; meanwhile ads must adhere to Google’s general advertising policies [ 18 ]. Second, there is the official support page for YouTube, which offers information on a range of topics, including “Create and grow your channel” and “Monetise with the YouTube Partner Program” [ 19 ]. Third, YouTube has created a so-called YouTube Creator Academy, in which much of the information found in its guidelines and support pages are presented as dozens of short “courses”, each subdivided into shorter “lessons”, often accompanied by videos, and ending in “exams” consisting of a number of multiple choice questions [ 20 ]. Lastly, YouTube operates a number of “official” YouTube channels, such as TeamYouTube [Help] [ 21 ], Creator Insider [ 22 ] and YouTube Creators [ 23 ]. Between them these channels have hundreds of uploaded videos, covering topics such as “What’s the Ideal Video Length”, “How to improve your YouTube recommendations and search results” or “MYTHBUSTING #4: Do Ads affect Search and Discovery (S&D)?”, often featuring well-known content creators or YouTube employees.

These features often take on a distinctively prescriptive tone, with titles or headings that begin with a verb in the imperative mood. For example, the support page titled “Discovery optimisation tips” has subheadings that implores the content creators to “Create descriptive and accurate titles and thumbnails”, “Keep viewers watching with video techniques”, “Organise and program your content” and “Use reports to see what's working” [ 24 ]. Similarly, the “course” titled “Grow your community” on Creator Academy asks the users to “Connect with your community”, “Reach beyond YouTube”, “Foster a positive community”, “Interact with your audience with new Community posts” and “Express yourself with Stories beta” [ 25 ]. Yet contradictorily, these features are also notable for their brevity in terms of concrete information regarding the workings of the Search and Discovery and Monetisation systems. For example, the support page titled “Monetisation systems or ‘the ads algorithm’ explained” is a single page of less than 400 words, which broadly states that “[o]ur systems look at your content and channels in different ways, and at different stages. For creators in the YouTube Partner Program, our monetisation systems can impact both your content and your channel”, followed by a few sentences describing how the systems scan for advertiser-friendly content, as well as viewer engagement [ 26 ]. The YouTube Creator Academy “lesson” on “Ads on YouTube” is equally laconic – it consists of a brief description of video advertising and different ad formats and a list of factors that can impact advertising (i.e. whether the content complies with Community Guidelines and Advertiser-friendly content guidelines, whether the content complies with copyright, and whether it is suitable for all audiences) [ 27 ].

The social infrastructure of YouTube also extends beyond the platform itself. Given the paucity of concrete information regarding the workings of YouTube’s systems, it is perhaps unsurprising that various online communities have sprung up to perform some function of collective sense-making. Many of these are to be found on the forum platform Reddit, with the largest being r/YouTube, with over 560 000 members, and dedicated to general “meta-discussion about YouTube as a platform, including its features, bugs, and business decisions”, for viewers and content creators alike [ 28 ]. Smaller forums include for example r/NewTubers and r/SmallYTChannel, with over 230,000 and 114,000 members, respectively, created specifically to provide collaborative resources and feedback for new creators wishing to refine their content and reach larger audiences [ 29 ].

A significant portion of the discussions on these forums are centred on the algorithmic systems of YouTube. The topic is often brought up in relation to issues important to many newer content creators – growing one’s channel and reaching new audiences. Thus, for example, user duugan asks on r/NewTubers:

Do YouTube and Google consider video length as ranking factor?

Currently, my videos are around 1:00 to 1:30 minutes, because I post them also in Facebook (shorter videos work better in Facebook for us). But I am now diversifying my video traffic source and would want to optimise my videos in YouTube.

Two users respond:

Since the algorithm changed, watch time is a big part of YouTube's determining factor for your videos' search and discovery. They favour videos with longer watch time, because views could be misleading or subject to abuse.

We all know they actually did it because. staying on a video longer means you're using their site longer which in turn gives a higher chance of you going onto the next video. – user Turtle_Co

YouTube favors videos with more watch time. I have some videos that are only a couple minutes […] I have some that are almost an hour. It's what is appropriate for the video. Don't force your videos to be longer for watch time. Audience retention is also a factor. My average videos are usually around 10 minutes and have decent audience retention. If you are making a video and it makes sense to have it longer, do it. Any length can be good if the video is engaging.

The main problem with mainly having short videos is having enough watch time to be or stay monetised […] you need a lot more views to get to the 4,000 hours annually. – user FandomSpotlite

[All quotes as originally appeared]

Similarly in response to one user’s question of whether changing video titles and thumbnails would improve traffic, one user responds:

Changing thumbnail hell yes, for obv reason, the better your thumbnail the better your CTR [click-through rate]. Thumbnail as no effect on search and discovery. So you can even change the thumbnail on your best video with weak CTR it can only get better.

Changing title hell yes again, the more searchable is your title the more you will get find. But contrary to thumbnail if you change your title the algorithm reset that video from a min or two up to few hours depending on how different the new title is. So probably worth doing that at 3 am, lol. I would not change the title on a high performing video because of the small algorithm reset. – user MaxSujy_React

It is also in communities like these that one often finds rather ambitious instructions or tutorial on how to “make it” on YouTube as a content creator, which nearly always address the platform’s technical systems in some manner, building on bits-and-pieces of information from YouTube’s communications, trade publications and personal experiences. For example, one widely shared post by user tlo_oly, titled “How To Gain 90,000 Subs In 3 Months [A Case Study]”, is over 5,700 words in length, and touches on topics ranging from social media platforms on which to promote one’s channel, monetisation strategies, YouTube’s Search and Discovery algorithms, to analytics data. One of the most popular posts on r/NewTubers, by user dangelowallace, titled “How I went from being a NewTuber to a […] Not-As-NewTuber (75k subs, 10 months)”, at close to 3 300 words, provides twelve pieces of advice, organised into trios of “Facts”, “Goals” and “Objectives”. Almost all of these posts adopt the form of step-by-step advices, ostensibly based on the personal experiences of content creators who have had some degree of success.

More ambitious still are the various attempts by lay users to reverse-engineer the YouTube Search and Discovery and Monetisation systems, for example those by Gielen and Rosen (2016) , Gielen (2017) and Sybreed and Sealow (pseudonyms, undated). These invariably rely on YouTube’s own application programming interface (API), and attempt to piece together a picture of the functionalities of the algorithms through automated data extraction. While some, notably Gielen and Rosen (2016) and Gielen (2017) , are published in trade publications, much of the research of this type are instead shared informally through discussions on platforms such Reddit, Twitter or YouTube itself.

The frequency and the popularity of these types of discussions attest to the important role that they play in the protocological system of YouTube. More than the terse and ambiguous language of official communications, the lay user’s understanding of the platform is often informed by these heuristics derived from informal research, educated-guesses, personal experiences and hearsays; and in the sense that these types of discussions affect the manner in which users interact with the platform and each other, they are as much a part of YouTube’s protocological system as the technical systems, official rules and guidelines and communications.

At the same time, while these types of contents are shared widely across communities, and receive hundreds of “upvotes” and comments each, a not-insignificant portion of the discussions in these communities show a spirit of resignation in relation to YouTube’s algorithms. For example, user digidv85 posts:

Sadly this has a completely negative effect on me. Right now I’m past the three year mark with my channel, I have 1,634 videos made in that time frame. Watch time for the past 12 months is 3.5K. Total views are 46,630. Yet my sub count is only 276. Nothing I do attracts people anymore. I’ve watched countless “help” videos on growing a channel. I’ve spent three months doing 50+ overhauls of thumbnail designs alone.

Likewise, user TheRealBluefire notes,

Hello I've had my channel since 2015 and it's been pretty slow, nowadays I'm only getting about 5 or 10 Views Tops and my channels stuck at 107 Subscribers, I've tried a few things like Uploading Two Videos a Week, Sharing My Videos On Social Media's and even adding as many tags as possible in my videos that fit the video itself but I'm still not doing well, is their anything I can do? I've always had the dream too become a Popular YouTuber and I feel like my dream might end.

Specifically, the tendency among many is to ascribe popularity on the platform partly or wholly to luck of the draw:

Sometimes it's just pure luck I mean I gave one of my music in a guy to put in his videos (i do that a lot) and he got to videos picked up by the algorithm maybe cause he was a channel with 100 subs back then and the videos were 30k - 60- views and that's when a lot of people came to me and I reached 1k on that track. – user KrzGhost

In a sense, the two sets of discourses reveal the core experience of many content creators on YouTube. On the one hand, YouTube’s own communications portray its algorithmic systems as straightforward, coherent and understandable. Creating and sharing videos are easy and accessible to all. Whether the user simply wishes to find an audience for their content or actually earning an income, success is portrayed as possible or even plausible, so long as one understands the system and puts in the requisite effort. This creates an environment which encourages content creation and sharing, and some basic level of compliance with the platform’s rules and guidelines, particularly in relation to advertiser-friendly content. Many of the user-created tutorials and guides, particularly those appearing on forums such as r/NewTubers, mirror YouTube’s own communications in their portrayals of the algorithmic systems as straightforward, coherent and understandable, albeit in far greater detail; and this is echoed by the vernacular culture of a significant portion of the content creator communities. The ephemerality of the creative process, of personal charisma, popularity, recognition, and success are brushed aside, and reduced and concretised to a series of steps, the first of which is understanding the algorithms. Yet on the other hand, understanding the system is precisely that which is difficult, because so little is revealed by the platform itself. What is known is pieced together from official communications, informal research on the system (which may or may not have changed since) and personal experience and best-guesses. And thus, beneath the veneer of the possibility of success is always the undercurrent of incomprehension and distrust – the system is arbitrary, ever-changing, largely unknowable and often indistinguishable from luck.

YouTube as a protocological system of control

Some patterns begin to emerge as we view YouTube’s various systems as protocological control. First, as Galloway and Thacker (2004 , 2007 ) note, protocols as a form of control exemplify the inseparability between life and life-as-information in an age in which interactions are increasingly mediated by information technology. Controls on YouTube rely on algorithmic surveillance of the behaviours of users, whether as viewers or content creators, and systems such Search and Discovery reduce said behaviours into combinable and calculable digital traces. As automated algorithms, Search and Discovery does not and cannot understand creativity and the myriad ways in which audiences engage with creative works. Rather, interactions between viewers and content creators are instrumentalised and quantified as “engagement”, primarily in the form of watch time. Individual users exist within the algorithms solely as particular sets of statistical generalisations, derived from the user-population as a whole – click-throughs, thumbnails, languages, tags, search histories, watch histories, feedbacks, demographics and geographic locations, all ultimately in relation to the central metric of “engagement”.

Second, YouTube as a system of protocological control is relational, that is to say it is manifest in the ways in which it facilitates users’ interaction. It is a system of rankings, calculations, and evaluations that is not so much concerned with referentiality towards objective criteria of “good”, but rather facilitates the creation of relationships between users, both as content creators and viewers, by generating and revealing a humanly comprehensible landscape of media content for each individual user, out of potentially billions of videos ( Kornberger et al. , 2017 ). In most concrete terms, the Search and Discovery system connects users by delivering user-generated content (primarily videos, but also comments, chat messages and even ads) to other users; it controls what users see on the platform. Yet it in no way guarantees that each and every video will be seen.

The crux of this lies in the fact that the platform does not facilitate interactions in a neutral fashion. The Search and Discovery system prioritises viewer “engagement”, particularly in terms of watch time, over all else. And this has had noticeable effects on the behaviors of users-as-content-creators and the types of contents created ( Bishop, 2018 ; Burgess and Green, 2018 ; Gillespie, 2010 ; Postigo, 2016 ; Rieder et al. , 2018 ). For example, it is widely believed that changes made to the Search and Discovery system in 2013–2014 were to a large extent responsible for the decline of animated videos, a popular genre in the early days of the platform. As one animator notes on the Reddit forum r/animation [ 30 ]:

The problem is YouTube itself. It's build for creators that can do a lot of new releases. Like video bloggers. At least once a week. Animation is very time-consuming field. We just can't afford to release that often and thus can't get enough money from channel monetisation to survive. I do myself animation channel and it's still alive (its non-english, so no link) and life is really tough for me as creator. “On the edge” I would say. And at the same time my buddy is making “talking head” chanell stupid as hell, just bla-bla-bla channel, but he survives pretty well 'cause he releases new videos every 4-5 days. That's reality now.

[Quote as originally appeared]

As Search and Discovery changed to prioritise frequent uploads and longer videos, original animated content became largely invisible within the system, as animators were unable to frequently upload new content due to the time-consuming nature of animation production. Search and Discovery controls the type of content that is visible on YouTube, and in the long run, “unpopular” content disappear from the platform as they become difficult, if not outright impossible, to discover.

In this sense, YouTube’s Search and Discovery and monetisation systems can be viewed as systems of controls geared towards the accomplishment of the organisational objectives, in this case the maximisation of watch time, by selectively facilitating certain types of interactions. This can be contrasted with the business model of traditional media organisations, which relies extensively on pitch meetings, test screenings and focus groups and stringent project management; YouTube on the other hand is, for a lack of a better word, more probabilistic in nature ( Bishop, 2018 ). The focus of YouTube as a platform is “bootstrapping and propagating viral content” ( Covington et al. , 2016 , p. 3), that is to say to encourage the creation and upload of content, to rapidly identify commercially viable media out of this vast library, and then with equal speed present them to a large audience. Rather than setting and communicating clear objectives and rules of conduct to ensure compliance on the part of individual content creators, YouTube’s system of protocols discloses enough information to facilitate some basic level of compliance in terms of the minimal acceptable set of behaviours, and crucially, to create the illusion of perceivable consequences and meaningful actions, and in turn, the possibility of success on the platform. The qualities of any particular video or channel are less important than being able to rapidly identify valuable content among the masses and present these to potential audiences.

Fundamentally, the power of the Search and Discovery system lies in a kind separation-through-sheer-abundance. Although it is possible to access any specific video directly through a shared link or the video’s URL, in practice interactions between content creators and viewers most often must rely on the Search and Discovery system. While creators may seek to promote their contents directly, for example through social media, in most cases they must rely on the Search and Discovery system to reach potential audiences. Moreover, this separation is not only apparent from the perspective of content creators. Whether using the search function or clicking on suggested videos, the viewer most often does not and cannot access the corpus of billions of videos directly. While new content creators struggle to connect with an audience, a large number of viewers complain of the difficulty of finding new content to watch – on the general YouTube discussion forum r/YouTube, a significant portion of the discussion around the algorithms centers around viewers’ experience of the Search and Discovery system, and the difficulty of finding new content. For example, in a thread on r/YouTube titled What is up with the recommendations algorithm? It mostly shows me videos I've already seen , users write:

Why do I keep seeing the same 30 videos (or so) day after day after day no matter what I watch? At some point when it does a “refresh” (no idea what triggers it), it latches onto whatever last few videos I watched, gets 1-2 similar videos for each, and keeps showing that set over and over and OVER. Even when I don't click them or add them to Watch Later […]

I should probably use “Not Interested” more, but it's […] It's all YouTube's fault for being exceedingly stupid. I'm not not interested in that stuff forever. You just can't fixate on a set of recommendations for so long. It's bonkers. – user Enamex

It’s becoming so much harder to find new and interesting content outside of the channels I’m already subscribed to.

I’ve been pigeonholed into an echo chamber of my own creation, with no way to get out. And its ruined YouTube for me. – user Dannyboi93

Also illustrative in this instance is the aforementioned function of the Super Chat. Chat messages during livestreams (live broadcasts) are one of the few ways in which viewers can interact (somewhat) directly with content creators, yet during popular livestreams dozens of messages scroll past in the chat window every second, far too fast to be read. For a small fee, users can purchase “Super Chats”, messages that are pinned to the top of the chat window. During livestreams, it is not uncommon for content creators to only respond to pinned Super Chat messages, if for no other reason than that of legibility. In other words, users are separated from each other by the sheer abundance of user-generated contents, whether in the form of videos, comments, or chat messages. The protocological systems of YouTube facilitates human interactions, but on its own terms. Interactions are always possible, but they come at a price, either indirectly, as when one views popular, monetisable content, or directly, as in the Super Chat.

Third, this protocological form of control exists in a state of dynamic tension between centralisation and distributed agencies. On the one hand, there is little doubt that YouTube maintains ultimate control over its Search and Discovery, Monetisation, and other ancillary systems as whole. On the other hand, the day-to-day functioning of the systems relies on the distributed agencies of its community of users and their collective sensemaking. This has a number of consequences for the platform as a whole. For one thing, the systems themselves exist in a state of tension between visibility and opacity – they are by no means secret, yet relatively little regarding their inner workings is revealed at any given time. Explicit feedbacks in the form of the “like” and “dislike” buttons are highly visible, yet the Search and Discovery system of today prioritises implicit signals such as watch time, watch-to-completion, and frequency of visit. Similarly, content creators are encouraged to tag their videos with a number of keywords, so as to aid the Search and Discovery system in identifying the content of the videos [ 31 ]. However, as Bishop (2018) notes, keyword tags were made invisible in 2012, so as not to mislead viewers. Visibility facilitates perceivable consequences and compliance, but also opens the systems to manipulation. Opacity and the constantly changing nature of the algorithms is part and parcel of protocological control – a system that cannot be understood and cannot be seen cannot be subverted. Yet it must remain sufficiently visible so as to retain the human interactions it requires for day-to-day functioning – compliance and cooperation become impossible if the system is perceived as overly arbitrary or indistinguishable from pure luck. Thus, the platform owner is constantly engaged in a back-and-forth between visibility and opacity, constantly tweaking the system and selective communicating changes to users.

Moreover, the protocol’s governance over interactions is not total. In one sense, some amount of workarounds is already built-in – once a particular channel or video is discovered, the subscription and share functions circumvent the Search and Discovery system entirely, by offering direct access to the content. The most tangible marker of success on YouTube is one’s number of subscribers, and indeed the platform awards physical trophies for 100 000, 1 million, and 10 million subscribers [ 32 ]. Paradoxically, becoming successful as a content creator on the platform involves attracting an audience, a community of users who access one’s content directly, outside of the structured interactions of the protocological system. In a broader sense, as YouTube’s system of protocological control extends beyond the boundaries of the platform itself, the boundary between the notions of the protocol and the counterprotocol is also blurred – even as the system reduces individuals and their activities to digitalised, calculable traces to facilitate and govern interactions, it does not succeed in imposing this power-knowledge relationship everywhere. Whether it is to promote one’s own channel and content, or to collectively make sense of the platform, even as users negotiate the system of YouTube, they discover means to connect elsewhere, on their own terms, on third-party platforms such as Reddit, independent of YouTube’s algorithms.

This is of course not to suggest that other platforms are free from protocols – Reddit, Twitter, Facebook each have their own set of algorithms that facilitates and governs interactions. In a Foucauldian sense, power and knowledge are always mutually dependent and mutually constitutive, and there is no fundamental mode of “life” that is discoverable and knowable independent of power structures – other platforms too will inevitably transform life into life-as-information so as to facilitate and govern, if only in somewhat different manners. Yet, the space between platforms offers the opportunity for different modes of interaction, not entirely governable by any one system of protocols. On the one hand, these interactions are inherently subversive, as they resist the co-option by any single system of protocols. Yet on the other hand, to the extent that platforms exhibit a high degree of mutual dependence – e.g. many platforms rely on YouTube for video hosting, while others use Reddit for its forum functions – it can be said that they too form an integral part of protocological control. Thus, the notion of the counterprotocol, that is to say interactions outside of the protocological system of control, is ultimately inseparable from that of the protocol.

In summary then, the findings of this study suggest that the control of users on YouTube is effected through a protocological system that places few explicit demands or restrictions on individual users, but instead selectively facilitate interactions between them. It does so through the algorithmic surveillance of user behaviours, and transform said behaviours into combinable and calculable data, all to maximise “engagement”, that is to monetisable interactions between users; it is a system that is not concerned with any objective criteria of quality beyond the possibility of a profitable match. It is equally worth noting that such a system extends beyond the formal, technological infrastructures of the platform; rather, it is also a social accomplishment in that it has to be made sense of and enacted by its users in their interactions, and is thus constantly reinterpreted and renegotiated.

Discussion and conclusion

While previous studies in accounting and management control on platforms have highlighted the role of evaluative practices in effecting neoliberal forms of government on these platforms and the constitution of users as entrepreneurial subjects, the nature of these market-like spaces and the interventions made to them by platform owners remain underexplored. While the marketplace of platforms may appear as a neutral space, it embodies the politics of those who hold power over such spaces and it requires continuous interventions so that it may continue to operate as if it is naturally self-regulating ( Munro, 2012 ; Cooper, 2015 ; Srnicek, 2017 ). In this sense, YouTube is the archetypal neoliberal marketplace – while presenting itself as a platform on which any creator can succeed on their own merits, the scope of interventions is near-total. And it is interventions in terms of strict control over interactions and the creation of relationships. Most interactions between users are facilitated, governed and surveilled by, and feed directly back into the protocological system of control. Platforms portray themselves as spaces where relationships can develop organically, but such relationships are in fact strictly regulated, on the platform’s own terms, towards the platform’s own goals

This conceptualisation of control in turn has implications for our understanding of the governance of these platforms in particular, and the neoliberal marketplace in general. First, it shows the limitations of (overt) evaluative practices as a frame of analysis in the study of platforms. Though applying different theoretical approaches, McDaid et al. (2019) and Van den Bussche and Dambrin’s (2020) studies of Airbnb both come to a similar conclusion – the mutual and public nature of the evaluation process results in largely positive reviews, thus rendering review scores themselves largely meaningless. When large number of objects receive similar review scores, what other devices are at work to direct users towards one object or another? Likewise, when platforms trade not in products or services, but evaluations as such from lay experts, as in the cases of Jeacle and Carter (2011) , Bialecki et al. (2017) , and Jeacle (2017), the experts, and by extension their evaluations, become objects of evaluation. Yet to what extent can this form of secondary evaluation be meaningfully carried out when platforms such IMDb and TripAdvisor offer huge numbers of user-generated reviews? As the present case demonstrates, the governance of platforms extends beyond the referentiality of evaluations. When a search query on any given platform is likely to generate hundreds, if not thousands, of results, which results are displayed and how matter. It is, in Foucauldian terms, the flow and circulation of objects and human capital, in a seemingly-random-yet-clearly-regulated fashion ( Foucault, 2008 ; Munro, 2012 ; Cooper, 2015 ).

Second, the platform user as a subject is in a peculiar place. While platforms transact in user-provided products, content and services, and are indeed reliant on user interactions for protocological control, ultimately the individual user matters little to the platform except as a set quantifiable traits that can be matched with those of another set of users. Success in the platform economy is probabilistic, it is statistically predictable but individually unpredictable – any user can in theory become successful; with the help of the algorithms some are certain to become successful; but not all can be successful. Moreover, this places new demands on the individual as participants in the platform economy. As the present case shows, considerable work on the part of the users’ centres around creating connections outside of the formal systems of the platform, whether it is to promote one’s content and gain subscribers or to collectively make sense of the systems themselves. To perform as a neoliberal subject goes beyond producing goods and services, but rather also involves managing one’s own movements and interactions in the marketplace. In a system mediated by technology, more than the ability to perform physical and intellectual labour, that which has been put to work is also human sociality – the ability to interact, to form relationships and communities. Whereas earlier studies by Jeacle and Carter (2011) and Scott and Orlikowski (2012) contend that the sense of community engendered an unique form of trustworthiness and accountability, the present case suggest that it is human connection itself that is monetisable.

Thus, Galloway and Thacker’s (2004) contention that the protocological system is always being in a state of tension, between the day-to-day functioning of the system based on decentralised agencies, and the centralised control over the system as a whole exerted by the owner/operator of the infrastructures, mirrors the social contradiction of neoliberalism in which ostensibly free, entrepreneurial subjects are constituted in a context fundamentally not of their own choosing, and over which they can exert little control ( Cooper, 2015 ). The notion of the subject being unique and free is essential for encouraging participation in the neoliberal marketplace of platforms, but also remains illusory, as this uniqueness and freedom of the individual conceal the coercive nature of opaque and non-negotiable systems of protocological control. This of course is not to say that platforms are in some ways uniquely neoliberal, but rather that they are deeply embedded in the dominant neoliberal rationalities of today, and that this is reflected in their technical and social infrastructures, and the ways in which these infrastructures shape our lives as social beings. Whereas Foucault saw biopolitics as the “empirical description of the government of population” under neoliberalism, to Galloway and Thacker (2007) , the systems of protocols of platforms exemplifies biopolitics in an age in which human interactions and relations increasingly come to be mediated by information technology.

This paper only scratches the surface of the issues at stake, and further research is needed on a range of topics relating management control on algorithmically controlled platforms. First, YouTube as a content platform is relatively unique. For example, interactions and transactions on the platform usually have very low stakes to the consumer of contents – it cost next to nothing, except time, to view contents or subscribe to channels. Nor are the majority of content creators interested in earning income from their videos. It would be interesting to extend protocological analysis to other platform organisations, where the nature of interactions is fundamentally different. Second, there is ample room for studies on the nature of subjectivity under automated, algorithmic control. Controls on platform are uniquely automated, opaque and non-negotiable ( Scott and Orlikowski, 2012 ). Consequently, this is an area that can benefit from more ethnographic research that directly explores the lived experiences of users of platforms. Third, Galloway and Thacker (2004 , 2007 ) theorise that resistance to protocols takes the form of counterprotocols – selectively manipulating and bending the protocols to other ends. There is already an emerging body of literature in the field of new media studies looking at how various actors have attempted to manipulate the protocological systems of YouTube for economical and political gains ( Lewis, 2018 ). Furthermore, the success of membership platforms such as Patreon, which offers the possibility of monetisation outside of YouTube’s Monetisation system, also suggests that resistance is mounting against what many content creators have beginning to see as digital exploitation on the part of YouTube ( Hern, 2018 ). This represents another area to which I believe critical scholars of accounting can make useful contributions.

It was not possible to study the monetisation functionalities of YouTube first-hand, as at the time of writing, monetisation is only available to users or channels with at least 1000 subscribers and 4000 hours of watch time over a 12-month period.

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Further reading

Anonymous ( 2017 ), “ Monetisation analysis/research ”, available at: https://docs.google.com/document/d/155yNpfR7dGKuN-4rbrvbJLcJkhGa_HqvVuyPK7UEfPo ( accessed 24 March 2021 ).

Acknowledgements

The author is grateful to the two anonymous reviewers for their thoughtful and constructive feedback on the manuscript.

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Fifteen years of YouTube scholarly research: knowledge structure, collaborative networks, and trending topics

• Published: 19 September 2022
• Volume 82 , pages 12423–12443, ( 2023 )

Cite this article

• Mohamed M. Mostafa 1 ,
• Ali Feizollah 2 &
• Nor Badrul Anuar 2  

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Since its inception, YouTube has been a source of entertainment and education. Everyday millions of videos are uploaded to this platform. Researchers have been using YouTube as a source of information in their research. However, there is a lack of bibliometric reports on research carried out on this platform and the pattern in the published works. This study aims at providing a bibliometric analysis on YouTube as a source of information to fill this gap. Specifically, this paper analyzes 1781 articles collected from the Scopus database spanning fifteen years. The analysis revealed that 2006-2007 were initial stage in YouTube research followed by 2008 -2017 which is the decade of rapid growth in YouTube research. The 2017 -2021 is considered the stage of consolidation and stabilization of this research topic. We also discovered that most relevant papers were published in small number of journals such as New Media and Society, Convergence, Journal of Medical Internet Research, Computers in Human Behaviour and the Physics Teacher, which proves the Bradford’s law. USA, Turkey, and UK are the countries with the highest number of publications. We also present network analysis between countries, sources, and authors. Analyzing the keywords resulted in finding the trend in research such as “video sharing” (2010-2018), “web -based learning” (2012-2014), and “COVID -19” (2020 onward). Finally, we used Multiple Correspondence Analysis (MCA) to find the conceptual clusters of research on YouTube. The first cluster is related to user -generated content. The second cluster is about health and medical issues, and the final cluster is on the topic of information quality.

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1 Introduction

Since its acquisition by Google in 2005, YouTube has been a video -sharing social media and a search engine with over 2 billion views per month [ 41 ]. It allows users to upload videos and share their content. It is a preferred search engine for contents like cooking recipes because of its audio and visual medium of communication. In addition to watching the videos, users can leave their comments and feedbacks for each video. The combination of audio, video, and comments make YouTube a valuable source of data. Researchers have been using this source of data to analyze various topics across wide range of research domains. One of the research domains that utilizes YouTube is health and healthcare. Educational videos and users’ feedback towards them have been a common research topic. For example, Li et al. [ 41 ] examined YouTube as a source of information on COVID -19 pandemic. Khatri et al. [ 34 ] also researched YouTube as a source of information on COVID -19 on English and Mandarin content. Hussein et al. [ 30 ] evaluated YouTube as a source of information by measuring the information on this platform and by auditing misinformation in videos. Indirectly, some research works developed methods that can be used in YouTube video analysis [ 42 , 56 , 57 , 45 ]. Analyzing research trends in YouTube papers requires the use of the bibliometric method.

Bibliometric is a quantitative analysis of papers published in a specific research domain [ 46 ]. The bibliometric study analyzes the authors’ activities, publication trends, and collaborations among institutions and countries. The bibliometric analysis evaluates impact of published papers and reveals the potential gaps and future directions in a research area, which increases interest and attention of researchers and funding bodies. The bibliometric study has been used in many research areas like COVID -19 pandemic [ 26 ], agricultural [ 47 ], accounting [ 50 ], and economic [ 8 ]. The advantages of using a bibliometric study are: 1) reveals important research works in a research domain; 2) helps to discover the gaps need to be addressed by researchers; 3) gives young researchers a holistic view of a research area.

To scrutinize research trends and direction on YouTube, this study aims at performing a bibliometric analysis on research works focused on YouTube published between 2006 and 2021. We propose the following research questions to fulfill the aim of this study: 1) what are the trends and directions in YouTube research? And 2) what information can be discovered related to YouTube research? The contributions of this study are as following:

we found only one published paper on YouTube bibliometric study, which presents number of papers, citations, and countries that published research works related to YouTube [ 56 ]. However, our work presents a more comprehensive analysis of the YouTube papers by providing network analysis, research structure, and thematic mapping.

we present a comprehensive network analysis like co -citation network, co -cited sources network, authors’ collaboration network, institutions’ collaboration network, nations’ network, as well as keywords and co -occurrence network.

We analyze the trending research and provide a structured research trends as well as thematic and historiographic mapping.

we adopt dominance factor, Bradford’s law, and Lotka’s law to analyze the published works using scientific methods.

This article is organized as follows. Section 2 describes the methodology used to carry out the analysis. Section 3 deals with research findings. Section 4 discusses the research findings. The last section deals with research limitations and explores potential avenues for future research.

This study is guided by the following four steps:

Selecting the database and defining the search terms.

Conducting the preliminary statistical analysis.

Performing the bibliometric network analysis.

Performing the conceptual structure, thematic and historiographic mapping.

To conduct the analysis, the R version 4.1 software [ 58 ] was used along with several libraries such as the bibliometrix, wordcloud and ggplot2 . For network visualization, we used the VOSviewer software [ 61 ]. We discuss here the steps outlined above in some detail.

2.1 Database and documents’ extraction

Following Sigala et al. (2021), the Scopus database was selected to conduct the analysis. As the largest database for peer -reviewed journals (Norris & Oppenheim, 2007), Scopus is frequently used by researchers to conduct bibliometric analysis (Cunill et al., 2019; Hassan et al., 2021). Having selected the database, we extracted bibliographic records related to the selected documents, including relevant information about documents’ titles, authors, and keywords. Retrieved documents were then transformed to a plain text format for further filtering and analysis. Choosing a particular type of document for bibliometric analysis has long been the subject of debate [ 51 , 52 ]. For instance, journal articles only have been selected in prior studies (e.g., [ 20 ]), whereas some authors have focused on both books and journal articles (e.g., [ 4 ]), yet others excluded only meeting abstracts, corrections, and editorial material, (e.g., [ 2 ]). Here, we opted for peer -reviewed articles only because such articles “usually undergo a meticulous peer -review process and are generally of high quality” ([ 16 ], p. 206). To avoid false -positive results, only article titles, abstracts and keywords were searched using the terms “YouTube.” Figure 1 plots the search procedure followed to extract the articles used in this analysis. We limited the selection to documents written in English and we chose 2006 as the date of reference because YouTube was launched in 2006.

Schematic flowchart of data acquisition and methodology (Adapted from [ 15 ])

Having selected the database, we extracted bibliographic records related to the selected documents, including relevant information about documents’ titles, authors, and keywords. Retrieved documents were then transformed to a plain text format for further filtering and analysis. Choosing a particular type of document for analysis has long been the subject of debate [ 51 , 52 ]. For instance, journal articles only have been selected in prior studies (e.g., [ 20 ]), whereas some authors have focused on both books and journal articles (e.g., [ 4 ]), yet others excluded only meeting abstracts, corrections, and editorial material, (e.g., [ 2 ]). Here, we opted for peer -reviewed articles only because such articles “usually undergo a meticulous peer -review process and are generally of high quality” ([ 16 ], p. 206).

Table 1 shows the main information about the YouTube research data.

The table reveals that 1781 research articles were extracted. The articles were written by 4699 authors, and they include 65,677 references. 417 articles were written by single authors, whereas 567 were written by multi -authors, with a collaboration index of 3.26. This index is calculated by dividing the total authors of multi -authored articles by total multi -authored articles [ 23 , 36 ]. Our result indicates that the average YouTube research team falls between 3 and 4.

2.2 Bibliometric network analysis

A network can be regarded as “a structure composed of a set of actors, some of whose members are connected by a set of one or more relationships” ([ 35 ], p. 8). In social network analysis (SNA), an edge connecting two nodes represents a relationship. Khan and Wood [ 32 ] noted that “when used to synthesize the existing literature from a network perspective, the SNA technique can reveal valuable invisible patterns that can certainly facilitate theory development and uncover areas for future research.” There has been extensive prior research using network analysis in areas as diverse as exploring individual scientific collaboration networks [ 11 , 27 , 66 ], collaboration among research institutions [ 21 ] and keywords co -occurrence networks [ 7 ].

2.3 Thematic and conceptual structure maps

Thematic maps or strategic diagrams were suggested by Law et al. [ 39 ]. The map is usually employed to reveal the clusters’ dynamics based on analyzing the keywords or co -word occurrences [ 29 ]. The Callon et al. [ 10 ] density and centrality metrics are generally used to construct the map. The map also draws heavily on the financial portfolio analysis and concepts based on co -word networks [ 5 ]. Due to its usefulness, the map has been used in a plethora of research articles [ 33 , 40 , 65 ]. On the other hand, conceptual structure maps can be employed to investigate the conceptual structure of a research area by breaking down a research domain into clear “knowledge clusters” [ 63 ].

3.1 Scientific output, core journals and impactful authors

We extracted 1781 Scopus documents related to YouTube. The documents were written by 4699 authors representing 70 nations. Timewise, the documents covered almost fifteen years (2006-2021). Figure 2 plots the scientific output trends in the field. Although the figure reveals an exponential annual growth rate, this rate is not evenly distributed. For instance, in the first two years there was a paucity in YouTube research with only a handful of papers per year. These two years might be referred to as “the initial stage in the YouTube research.” However, the next decade (2008-2017) appears to witness a tremendous increase in research dealing with YouTube. This decade might be called “the rapid growth stage.” Indeed, this period represents the highest growth rate. The final stage (2018-2021) might be called the “consolidation and stabilization stage” because the YouTube research reached the “saturation/maturity” stage. This result is in line with several bibliometric studies conducted in several research areas [ 53 , 66 ].

YouTube research annual scientific production (2006–2021)

Table 2 shows the most important Scopus -indexed journals publishing YouTube research. The table reveals that the most relevant sources publishing YouTube research include journals such as New Media and Society, Convergence, Journal of Medical Internet Research, Computers in Human Behavior and the Physics Teacher . Another way to examine the journals’ influence is known as the Bradford’s law [ 37 ]. This law was first proposed by Bradford [ 9 ], who noted that “if scientific journals are arranged in order of decreasing productivity of articles on a given subject, they may be divided into a nucleus of periodicals more particularly devoted to the subject and several groups or zones containing the same number of articles as the nucleus.” Fig.  3 plots the Bradford’s law in YouTube research. From the graph, we see that the “core zone” is dominated by just few journals, including New Media and Society, Convergence, Journal of Medical Internet Research, etc. Such journals are considered the outlets publishing the “core” YouTube research.

Bradford’s law in YouTube scholarly research

The YouTube research growth is also evident from the corresponding author’s country involved (Fig.  4 ).

YouTube research by corresponding author’s country. Note: SCP = Single Country Production; MCP = Multiple Country Production

Table 3 shows the most cited articles in YouTube research. The table shows that Smith et al. (2012) paper in the Journal of Interactive Marketing is the most cited paper as it was cited 457 times. In this article, the authors compared brand -related user -generated content between three social media platforms, namely Twitter, Facebook, and YouTube. Results provide a general theoretical framework demonstrating how consumer -generated brand communications are influenced by a particular social media channel. The second most cited paper (443 citations) is Lang (2007) paper published in the Journal of Computer - Mediated Communication. In this paper, the author employed ethnographic methodology to analyze how YouTube participants develop and maintain social networks related to video sharing activities. With 333 citations, Susarla et al. (2011) article is the third most cited paper. In this article published in Information Systems Research , the authors analyzed the networked structure of interactions on YouTube. Results revealed that “social interactions are influential not only in determining which videos become successful but also on the magnitude of the impact.” (p. 23). Halpern and Gibbs (2013) paper published in Computers in Human Behavior was cited 298 times. In this paper the authors used two social media platforms, namely YouTube and Facebook to examine how social media can be used to foster democratic deliberations. Results showed that the “Facebook expands the flow of information to other networks and enables more symmetrical conversations among users, whereas politeness is lower in the more anonymous and deindividuated YouTube” (p. 1159). Khan’s (2017) paper published in Computers in Human Behavior was cited 291 times. In this paper the author investigated motives behind YouTube users’ engagement. Results revealed that YouTube participation is driven mainly by the relaxing/entertainment motive. However, passive content viewing was mainly driven by reading comments posted on the platform. Table 4 .

The dominance factor is a bibliometric measure that calculates authors dominance by dividing the number of multi -authored articles in which the author is the first author by the total number of multi -authored articles [ 38 ]. This metric has been used widely in the literature [ 23 , 25 ]. Figure 5 shows the dominating authors over time. From the figure, we see that the most dominating authors were C Basch from 2015 till 2021, Riendeau from 2009 till 2012 and S Azer from 2012 to 2021. Newcomers to the field have also achieved some dominance. Examples include J Yin (2017-2019) and J Park (2016-2021).

YouTube authors dominance over the time

In bibliometric studies, “Evenness/concentration of authors’ contribution” is a widely used metric [ 49 ]. This metric can be quantified using Lotka’s law (Lotka, 1926). Based on the well -known Zipf’s law, Lotka’s law implies that “the number of authors producing a certain number of articles is a fixed ratio, 2, to single -article authors.” Results suggests that the Lotka’s law seems to hold in YouTube research ( K - S two sample test p  > 0.05).

3.2 Network analysis

3.2.1 co -citation networks.

A co -citation network is formed when two authors are cited together in a third reference. Figure 6 displays the YouTube research co -cited authors’ network. Based on the color used, the graph reveals four distinct clusters. The red cluster includes authors such as J Burgess, M Thelwall and J Green. The size of the node indicates which author occupies a central position in the cluster. Such author(s) might be regarded as influential as they have disproportionate impact on the information diffusion on the network [ 6 ]. From the graph, we also see that some nodes are quite close to each other, whereas others drift further away. McPherson et al. [ 48 ] argued that closeness signifies a strong “homophily effect,” which occurs when authors in a virtual -room -like environment discuss common topics [ 24 ]. In bibliometrics, homophily is an indicator of “disciplinary or thematic similarity” [ 31 ]. For example, the nodes representing both R Schatz and A Finamore are very close to each other, indicating possible “homophily effect.”

YouTube authors co-citation network (> = 30 articles)

The green cluster includes authors such as C Basch, J Keelan, A Pandy and S Sarangi. The blue cluster includes sixty -two authors such as J Baker, D Charnock, A Rapp and J Lee. The yellow cluster is the smallest and it includes ten authors such as A Finamore, R Schatz and J Wang. The centrally located authors in each cluster might be regarded as influential authors as they “tend to anchor each community and they have a large impact on other communities as they control and stimulate information diffusion [in the network] through research activities” ([ 53 ], p. 664).

Figure 7 displays the YouTube research co -cited sources’ network. The graph reveals five distinct clusters. For example, the Journal of Clinical Rheumatology, Epilepsy Behavior and the Journal of Cancer Education are co -cited together as they belong to the same cluster. The American Sociological Review is co -cited with Discourse and Society, and Feminist Media Studies . The Journal of Advertising is co -cited with the Journal of Business Research and the Journal of Consumer Research , whereas Body Image is co -cited with the Journal of Pragmatics and Sex Roles . Interestingly, “core” journals occupy central position in the network with a minimal interaction among the distinct clusters, confirming what Glotzl and Aigner [ 28 ] term “the orthodox core -heterodox periphery” phenomenon within the field of YouTube research. Dobusch and Kapeller [ 22 ] found that “orthodox journals” tend to be heavily cited, whereas “heterodox journals” tend to be drifted towards the periphery.

YouTube source co-citation network (> = 30 articles)

3.2.2 Collaboration networks

The collaboration network among authors is depicted in Fig.  8 . The thickness of the link in this graph is proportionate to articles coauthored, whereas the node size is formed based on the author’s publications. A glance at the graph reveals that the sparse network is formed by seven distinct communities, signifying a limited cooperation among authors. The sparse network implies that impactful researchers in the field work in isolated “silos” [ 62 ].

YouTube authors’ collaboration network (documents > = 2 articles)

Figure 9 depicts the collaboration network at the institutional level. The thickness of the link is proportional to the institution’s collaboration, whereas the node size is formed based on each institution’s publications. From the graph, we see that there are seven distinct clusters. For example, there is a strong collaboration between Columbia University, the New York University and the William Paterson University in the US. Zou et al. [ 66 ] argued that this type of sparse collaboration reflects a “locally -centralized -globally -discrete” cooperation. It also reflects a “North -South” divide, with a clear lack of cooperation between developed/developing world institutions.

Collaboration network among institutions producing YouTube research (documents > = 1 article)

Figure 10 shows the collaboration at the nations’ level, with a total of 62 nations collaborating in the scientific production of YouTube research. The figure shows that US tops the world in terms of the total collaboration links, followed by the UK and Australia. A closer look at the graph reveals that some clusters are formed based on geographic distance or linguistic similarity. For example, Spain cooperates with Colombia, Ecuador and Mexico. The cluster that includes Egypt also includes Kuwait and Saudi Arabia. Figure 11 plots the “geographic atlas” of the countries producing the YouTube research.

Collaboration network among nations producing YouTube scholarly research (documents > = 2 articles)

Geographic atlas of collaboration among nations producing YouTube scholarly research

3.2.3 Keywords and co -occurrence network analysis

Due to their abstract nature [ 12 ], keywords can be used to reveal the content of a paper. Figure 12 shows a simple wordcloud constructed based on the author -provided keywords. A wordcloud plot is an appealing visual tool that can be used to summarize textual data. The size of each word and its closeness to the cloud center determine its significance [ 42 , 43 ]. From the figure we see that the most relevant/frequent keywords used are “Youtube”, “social media” and “Internet.”

Keyword-based wordcloud of the most frequent YouTube terms

To further scrutinize how frequently keywords co -occur in the same document, we also used the author -provided keywords to construct the YouTube keyword co -occurrence network because “authors of a paper should be the ones that have the best feel as to what areas are spoken to by the paper” [ 19 ]. Figure 13 displays the resulting co -occurrence network. The graph reveals eight main clusters. For example, the first cluster in blue deals with medical/health use of the YouTube and includes words such as “health communication”, “health education” and “health information”. The second cluster (green -colored) deals with consumer comments and includes words such as “user -generated content”, “social network” and “Web 2.0”. The third cluster (yellow -colored) deals mainly with the educational use of the YouTube and includes words such as “e -learning”, “medical education” and “online videos”.

Co-occurrence network for author-provided YouTube keywords

A three -field plot, also known as a Sankey diagram, was also used to contextualize the flow trend linking keywords (left), authors (middle) and sources (right). In this diagram the size of the boxes is proportional to the related quantity (keyword, author, or source). Figure 14 displays the YouTube research Sankey diagram. Not surprisingly, edge widths flowing from keywords as “YouTube”, “social media”, and “Internet” are the largest, signifying that such keywords were used by several authors in their publications. We see also see that while some authors have used an extensive list of the keywords reflecting the diversity of their research (C Basch), others used a unique keyword (J Kim).

Sankey diagram for YouTube research flow (kewword-author-reference)

3.2.4 Trending topics and thematic evolution

Figure 15 plots the major YouTube research trending topics. From the graph we see that there is a move from established YouTube topics such as “video sharing” (2010-2018) and “web -based learning” (2012-2014) to new topics such as “COVID -19” (2020 onwards) and “misinformation” (2020 onwards). Such topics might be regarded as “trending topics/hotspots” in the scholarly publications dealing with YouTube because it has been argued that trending topics usually represent hotspots or evolving themes in a specific research domain [ 13 , 14 , 54 , 60 ]. Abrupt burst or surge in keywords might be also an indicator of “potential fronts” [ 57 ] as “the body of knowledge in a certain discipline can be seen as a sequence of topics that appear, grow in importance for a particular period and then disappear” [ 18 ].

YouTube research trending topics

3.3 Conceptual structure and thematic maps

We applied the Multiple Correspondence Analysis (MCA) method on the author -provided keywords. The MCA is an extension of correspondence analysis, akin to the Principal Component Analysis (PCA), that helps to analyze the pattern of relationships of categorical data [ 1 ]. It was selected since the results of this method is proved to be better on categorical data compared to other methods [ 1 ]. Figure 16 depicts the resulting YouTube research conceptual structure over four decades. From the graph, we see that the best dimension reduction achieved for the first two dimensions of the MCA account for roughly 72% of the total variability. In this graph, the closer the dots, the similar the profile they represent, whereas each cluster of dots represents discriminating profiles [ 64 ].

Conceptual structure map for YouTube scholarly research (MCA method)

An inspection of the graph reveals the depth and breadth of the domain. For instance, the largest red cluster comprises keywords emphasizing the consumer -generated content such as “user -generated content”, “web 2.0” and “online video.” The second cluster (in green) appears to deal with health and medical issues and includes keywords such as “health communication”, “health information” and “misinformation.” The third cluster in blue appears to deal with YouTube research within the context of information quality and includes keywords such as “internet”, “information” and “quality.”

A thematic/strategic map is also shown in Fig.  17 . In this graph, average values of both axes are represented by a dotted line dividing the map into four quadrants. Each quadrant in this graph represents a different theme, whereas the bubble size is drawn in proportion to the frequency of documents in which the keywords is used. The first quadrant represents “motor themes” that are well -developed both internally and externally as it is characterized by high density and centrality. [ 17 ]. Within the YouTube research, such themes include “user -generated content”, “new media”, “influencers”, and “gender.” The second one is usually labeled the “highly -developed -and -isolated themes” quadrant as it deals with niche themes. With high -density -low -centrality structure, this quadrant highlights the fact that while the themes it comprises are well -developed internally, they are marginally important externally. Within the YouTube research, such themes include “education,” “medical education”, and “technology.” The low -density -low -centrality third quadrant is termed the “emerging -or -declining themes” quadrant. This implies that the themes in this quadrant are characterized by weak ties at the internal and external levels. Such themes might indicate potential hotspots in YouTube research. Examples include “COVID -19”, “health communication” and “Twitter.” Finally, the “basic -and -transversal themes” quadrant (low density -high -centrality) comprises themes that are weakly developed in terms of internal ties. Nevertheless, they are characterized by important external ties. Within the YouTube research, such themes include “Social media” and “internet.”

YouTube research thematic/strategic map

4 Discussion

This study examined published research works related to YouTube between 2006 and 2021. At this point, we can answer the research questions. To answer the first research question about trends and directions, we found that between 2006 and 2008, there was a slow growth in publications since the YouTube platform was new. Then from 2008 to 2017, there was a rapid growth in research on YouTube. Afterwards, the trend is still upward with a slower pace. We also found that the trending topic changes over time. While “gaming” and “video sharing” were trending topics in some time period, the trend shifted towards topics like “COVID -19” and “misinformation”.

The second research question is related to the information discovered from YouTube research. We discovered the most cited papers, authors, and countries with highest number of publications. We also discovered the network between the published works. Specifically, the authors’ collaboration network, collaboration between institutions, and collaboration between countries. We also analyzed the collected works regarding the Bradford’s law and Lotka’s law. It was proved that large number of papers were published in a small group of journals, which followed the Bradford’s law. Also, it was proved that the frequency indexes of author productivity distribution followed Lotka’s law. Additionally, the MCA algorithm was used to find the conceptual structure map related to YouTube papers. The output shows three clusters, consumer -generated content, health and medical issues, and information quality.

Based on this paper’s results, large number of works are related to health and medical issues. Among the institutions, department of public health appeared more than other institutions. Additionally, the journal of medical internet research is in the third spot of the most relevant sources. The MCA algorithm dedicated one cluster for health and medical issues. Furthermore, “medical education” topic started trending in 2014 and is still trending, based on Fig.  15 , which is one of the longest trending topics. It is clear that researchers are interested in analyzing YouTube about health -related issues. These points coincide with studies on effectiveness of YouTube videos as a health educational platform. Allgaier mentioned that many people use YouTube as a source of information on science, technology, and health [ 3 ]. It is also assumed that because of the sensitivity of health and medical related issues, researchers focused more on the health aspect of the YouTube to find information and misinformation in videos. They analyzed videos and comments to understand users’ feedback on the health -related videos [ 59 ].

5 Limitations and future research

Despite the major contributions of this study, it suffers from some limitations. First, we relied only on the Scopus database to conduct our bibliometric analysis. Thus, we unavoidably commit a selection bias. Subsequently, we believe that future research should test the robustness of our finding by merging several databases such as WoS and Google Scholar. However, it has been argued that the Google Scholar database is less stringent as it comprises citations from unpublished manuscripts, blogs, etc. (Gavel & Iselid, 2008; [ 55 ]). Second, we limited the selection of documents to articles published in English. Thus, our results might be limited in terms of coverage [ 57 ]. Future research might add other languages to test the generalizability of our findings. Finally, although we conducted a comprehensive study on the whole domain of YouTube research, future research might focus on specific journals publishing YouTube research such as New Media and Society, Convergence, Journal of Medical Internet Research, Computers in Human Behaviour, and the Physics Teacher, among others.

6 Conclusion

This work conducted a bibliometric study on YouTube, as a research topic, in the literature between 2006 and 2021. The search in Scopus database resulted in 1781 research works, which were collected along their meta data such as authors name, keywork, etc. The collected data were analyzed, and the results were presented in the form of network of collaborations between authors, institutions, and countries. We also show the results of networks of keywords. We then created a thematic map based on the keywords to find the trending topic in research related to YouTube. The analysis revealed that 2006 -2007 were initial stage in YouTube research followed by 2008 -2017 which is the decade of rapid growth in YouTube research. The 2017 -2021 is considered the stage of consolidation and stabilization of this research topic. We also found that the trending topic changes over time. While “gaming” and “video sharing” were initially trending, the trend shifted towards topics like “COVID -19” and “misinformation”.

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Mostafa, M.M., Feizollah, A. & Anuar, N.B. Fifteen years of YouTube scholarly research: knowledge structure, collaborative networks, and trending topics. Multimed Tools Appl 82 , 12423–12443 (2023). https://doi.org/10.1007/s11042-022-13908-7

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Received : 13 December 2021

Revised : 21 February 2022

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Issue Date : March 2023

DOI : https://doi.org/10.1007/s11042-022-13908-7

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