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This article is part of a larger series investigating the gender gap on YouTube.
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In 2008 YouTube won a Peabody Award. Handed out yearly at the Waldorf Astoria in New York, the Peabodies honor excellence in journalism and media. In 2008, this prestigious award was bestowed on the three-year-old video streaming platform.
In describing why YouTube received the award, the Peabody program declared that the platform was “a digital ‘Speakers' Corner’ that not only embodies but also promotes democracy.”
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In its early days, YouTube offered a space where individuals could freely express themselves, connect with others globally, and bring niche communities into the spotlight. However, as YouTube evolved, so did its complexities. The rise of algorithms fundamentally shifted the viewing landscape, shaping the content consumption experience in a massive way and contributing to gender disparity on the platform. In this article, we’ll delve into the role of algorithms in shaping viewing habits on YouTube and examine how they have contributed to the gender gap in success on the platform.
YouTube's development mirrors the internet’s evolution. Initially, the web was an open space, where users explored freely, similar to wandering through a vast library. The advent of search engines and social media platforms introduced algorithms designed to streamline and personalize the user experience, offering tailored content recommendations based on user behavior, preferences, and demographic information.
While these algorithms enhanced user experience, they also controlled the flow of information. They quickly became instrumental in deciding what videos were seen and popularized. Today, the algorithm reigns supreme and dictates nearly every aspect of the platform.
What exactly is the YouTube algorithm, and why do some people find it so problematic?
Entrepreneur and tech researcher Ola Kowalewski explains
“An algorithm in itself is any set of defined steps programmed for some output. A classic example is making toast with butter: step one, get bread; step two, cut the bread; step three, put it in the toaster. Algorithms don’t need to be computer-based. But when we’re talking about artificial intelligence, we’re focusing on computer systems. The YouTube algorithm, or any recommendation algorithm, is a subset of AI called machine learning. At the most basic level, it’s a model that ingests a lot of data and ideally improves over time.”
The YouTube algorithm, a computer program, is supposed to be neutral, providing recommendations based purely on data. But is it truly unbiased?
Ola explains:
“There’s no way to make an algorithm bias-free. Algorithms are inherently biased because humans are inherently biased. Our biases stem from everything—our environment, culture, and even genetics. The key is being aware of the biases inherent in the algorithm’s design.”
Consider the backlash against AI image generators like DALL-E and MidJourney. When these tools launched, they perpetuated stereotypes. For instance, a prompt for a “CEO” overwhelmingly generated images of white men in suits. Bloomberg reported that 94% of the time, a CEO prompt returned images of men.
As Ola notes, algorithms are created by humans. Safiya Umoja Noble, in her book Algorithms of Oppression, argues that search algorithms reflect societal biases and discriminatory practices. Noble’s research revealed troubling disparities, such as how a Google search for “Black girls” once surfaced sexually explicit terms, unlike searches for “white girls.”
Google eventually addressed these issues, but they highlight how societal biases infiltrate technology.
Ola identifies three key elements contributing to algorithmic bias and gender disparity on YouTube:
“Every click we make reinforces the current bias. For instance, if I unconsciously favor male reviewers for tech products, that’s reflected in the algorithm.”
In 2011, YouTube shifted its algorithm to prioritize high click-through rates and extended watch times. This change inadvertently favored sensationalized content, which often evokes strong emotional responses like anger or outrage. Such content keeps viewers engaged, maximizing ad revenue.
Filmmaker Alex Winter’s documentary The YouTube Effect delves into how sensationalized content drives the algorithm, with 70% of all videos watched on YouTube being algorithm-recommended.
In the film, Winter interviews Hany Farid, Professor at the UC Berkeley, Computer Science and School of Information. He says:: “These platforms are in the engagement business. They extract data and deliver ads. Algorithms perpetuate problems, creating echo chambers and rabbit holes.”
A 2022 study by Australian academics Elise Thomas and Kata Balint sheds light on troubling trends in how YouTube’s algorithms promote misogynistic, anti-feminist, and other extremist content to young men in Australia. The researchers created 10 experimental accounts to observe how the algorithm shaped their recommendations over time.
As the accounts engaged with content—viewing and liking videos—the algorithm began recommending increasingly antagonistic messages toward women and feminism. Over time, these recommendations escalated into overtly misogynistic “Manosphere” and “incel” content. Disturbingly, even the baseline accounts—created without specific engagement—were quickly recommended videos critical of feminism and gender equality. In all cases, this occurred within hours of account creation.
These findings highlight how YouTube’s recommendation system amplifies content that reinforces harmful gender stereotypes. But this issue isn’t new—it’s the result of years of algorithmic evolution.
Tech journalist Taylor Lorenz has written extensively about the shifts in YouTube’s algorithm and how they shaped the platform’s content. In a conversation about these changes, she traced the roots of today’s male-dominated creator landscape to a critical change in 2011.
“So around the middle of the 2010s, you saw a mass exodus from Vine, which was very male-dominated,” Lorenz explains. “A lot of those Vine creators spilled over to YouTube at a time when YouTube began to reward frequency of content—daily posting. You saw Casey Neistat really begin this daily vlogging trend, and publishing daily became one of the fastest ways to grow. Male content creators leaned into this with prank content, which was repeatable, low-effort, and easy to produce.”
From 2015 to 2018, prank content exploded on YouTube. This genre, largely dominated by men, often featured juvenile and misogynistic humor. Lorenz recalls examples like Logan Paul’s notorious “kissing pranks” at Venice Beach, which involved harassing women without their consent. Female creators who attempted to break into the space, such as Elle Mills, faced intense criticism and double standards. Their humor was dismissed, their femininity questioned, and their appearances scrutinized.
Women creators also struggled with YouTube’s preference for younger talent. Older women, especially those transitioning from Vine, found it nearly impossible to gain traction. “It’s hard for women to age on the platform,” Lorenz observes. “Older male creators are seen as senior or experienced, while older women are dismissed as washed up. They lose brand deals, distribution, and audience interest.”
The shift in YouTube’s algorithm also created a feedback loop favoring sensationalized, male-oriented content. Gaming and prank videos, which kept viewers engaged for longer periods, were easy to produce and upload frequently. These videos were prioritized by the algorithm, leading to higher ad revenue for creators. With more resources, these creators could reinvest in their content, solidifying their dominance on the platform.
This system of algorithmic reward has given rise to a new class of creators, many of whom fit a specific mold. Among them, one name stands out: MrBeast, the platform’s most successful creator. Examining his meteoric rise offers insight into why male creators dominate the upper parts of YouTube.
MrBeast, born Jimmy Donaldson, is best known for his extravagant challenges, philanthropic stunts, and massive giveaways. In one standout video, he paid for cataract surgery for 1,000 people, allowing them to regain their sight.
With nearly 300 million subscribers, MrBeast is the most followed YouTuber in the world. His influence extends beyond YouTube—he recently secured a $100 million deal with Amazon for a game show and graced the cover of Time magazine, which hailed him as “The most watched person in the world.” His ubiquity rivals that of Donald Trump or the Kardashians, with constant media coverage ranging from the launch of his burger chain to his chocolate company to his declaration of a future presidential run.
This massive success has made him a role model for creators eager to emulate his rise to fame. But with success comes imitation—and criticism.
In a 2022 interview with YouTubers Colin and Samir, MrBeast reflected on his impact, particularly the phenomenon dubbed “MrBeastification.”
Colin:
“People talk about the ‘MrBeastification’ of YouTube, where creators copy you—thumbnails look the same, concepts look the same. For a while, it felt like every video had a thumbnail with a different person’s face, but the same style.”
MrBeast:
“I wish people would... there's inspiration, and then there's stealing. So many people just steal what I do—literally take my thumbnails or mimic my opening lines. It’s brutal. I’ve shared everything I know about succeeding on YouTube, and while some creators have used that to grow positively, others just copy my formula exactly. It’s frustrating.”
MrBeast’s success has defined what works on YouTube. But the kind of content he pioneered is distinctly male-dominated. While his philanthropy is commendable, it’s also fair to say he’s established a framework that benefits a specific “in group”: overwhelmingly male, largely white, and predominantly straight creators.
MrBeast often collaborates with a close-knit team, primarily male, with women making up just 17% of those featured in his videos. This isn’t unique—similar male-centric collectives like the Sidemen, FaZe Clan, and David Dobrik’s Vlog Squad dominate the platform. These groups frequently create content relying on shock value, which can be hostile to women, whether through objectification, offensive humor, or outright exclusion.
While MrBeast’s content is relatively clean, his success illustrates a challenge for female creators. His blueprint—stunt-based, challenge-driven videos—is overwhelmingly male-dominated. Few women have found success replicating it, while countless men have thrived by following his formula.
MrBeast attributes his success to mastering the algorithm. As he explained in a Joe Rogan interview, he obsessively studies YouTube’s recommendation system. But the algorithm isn’t just shaped by creators like MrBeast—it’s also shaping them.
Millions of fans watching his videos signal the algorithm to promote similar content, creating a self-perpetuating cycle. This entrenches the dominance of male creators producing MrBeast-style videos, while sidelining content that doesn’t fit the mold.
MrBeast didn’t create the gender disparity on YouTube, but his success has reinforced it. His model of success rewards content that appeals to the algorithm—content that skews overwhelmingly male. This feedback loop doesn’t just reflect the algorithm’s preferences; it reinforces them, making it even harder for female creators to succeed.
Ultimately, the problem lies with YouTube’s algorithm, which continues to privilege a narrow, male-dominated vision of success. For all its claims of democratizing content, YouTube’s design is perpetuating inequality—and that’s something the platform must address.
Addressing YouTube’s gender gap requires collective action from creators, advertisers, and regulators:
New York-based attorney Carrie Goldberg sums it up:
“The internet isn’t just a mirror to society. It’s a weapon. Platforms profit from harm. They must take responsibility.”
YouTube has the potential to reshape itself into a fairer, safer platform. By addressing algorithmic biases and fostering gender equity, the platform can honor its original promise of being a true democratized “speakers corner” for all.
This article is part of our series investigating gender disparity on YouTube, culminating in our podcast Who Killed the Female YouTube Star?
You can find out more about the podcast here and can listen to it here.
