YouTube’s recommendation system causes users to encounter completely different content streams on the same platform. This structure, shaped by viewing history, likes, subscriptions, search behavior and Google account activities, has brought a new debate to the agenda, especially about gender-based content separation. How do YouTube recommendations create different worlds? YouTube determines the videos shown on the home page and Next section according to user behavior.
According to the platform’s own description, the system; It takes into account signals such as viewing history, likes, dislikes, subscriptions, user feedback and satisfaction surveys. This structure differentiates each user’s home page. Two people who open YouTube at the same time can encounter a completely different video universe, even if they live in the same country and speak the same language. This separation is not limited to their areas of interest.
Users’ gender, age group, viewing habits, and behaviors that align with gender stereotypes can sharply change the types of content offered by the recommendation system. While a user may quickly turn to content focused on personal care, lifestyle, relationship or family, another user may more frequently encounter videos featuring technology, finance, sports, politics, masculinity discourses or controversial figures. At this point, one of the most striking topics is the content shown to young male users.
Research conducted by the Institute for Strategic Dialogue created test YouTube accounts representing young men and boys. While some of these accounts were fed with different ideological contents, some were initially left blank. In the research, it was seen that content defined as anti-trans, misogynist and “manosphere” was recommended to all accounts, regardless of their ideological level. The research stated that videos, especially those featuring names such as Jordan Peterson and Ben Shapiro, worked as a gateway to a wider flow of anti-feminist and anti-woman content in some accounts.
Researchers stated that although these videos were not included in the initial lists, they were recommended to test accounts in the main YouTube experience and on the Shorts side of the platform. In one of the accounts left empty, it was noted that the recommendation system reacted faster to user behavior and in a short time, a line of suggestions ranging from Sigma Male content to extremist visual cultures was formed.
This table reveals that YouTube does not show the same internet to everyone. The platform’s interface remains the same, but the content flow varies from person to person. When the user watches certain types of videos for a while, the system takes this behavior as a signal and highlights similar content more. Thus, the user may encounter content that he or she is not actively searching for. YouTube also offers control tools in this regard.
Users can delete their viewing history, pause the history, use the “I’m not interested” option for certain videos, and request that certain channels not be recommended again. YouTube also states that home page recommendations may be limited when watch history is turned off or there is not enough history. In other words, user history is at the center of the recommendation system. However, the discussion is not limited to technical settings only.
Recommendation systems do not work like a passive list that determines what users will watch, but also shape which ideas, which social debates and which content clusters users will encounter more frequently. For this reason, the content bubble issue on YouTube is becoming an important topic not only in terms of personalized entertainment flow but also in terms of the social impact of the platforms. On the academic side, the filter bubble and echo chamber debate is not one-sided.
While some studies argue that recommendation systems confine users to similar content clusters, some literature reviews are more cautious about the claim that large user bases live in completely closed information bubbles. Despite this, the impact of algorithmic recommendations is being examined more closely, especially for young users, children, groups consuming political content, and user profiles exposed to gender-based content.
At the point where YouTube has reached, the recommendation system no longer just selects the next video. It also organizes the parts of the world that the user sees during their time on the platform. This arrangement is sometimes limited to music, game, technology or movie recommendations. In some scenarios, it moves the user to a narrower content area around gender, identity, relationships, masculinity, feminism and social debates.
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