The internet seems to constantly ask the same question: How is it that a country song, which many claim they have never heard before, composed by a singer whom they have also never heard of, managed to set the record for the longest time at the top of the charts in the 100,68-year history of Billboard Hot?

Ella Langley's " Choosin ' Texas " topped the charts for a cumulative 24 weeks, surpassing the record of 22 weeks held by Mariah Carey's " All I Want for Christmas Is You ". The latter is one of those Christmas classics that you can hardly avoid hearing every winter. This consecutive reign came to an end on the chart of October 10th, when Taylor Swift's " Patient Zero " soared to number one. Carey's song usually returns to the top in December each year, so it will need to win another 3 weeks to reclaim that record.

Data shared by entertainment data and research company Luminate with Fortune shows that in the second quarter of 2026, only 24% of American music listeners were aware of Ella Langley. In contrast, this proportion was 13% in the fourth quarter of 2025 and 17% in the first quarter of this year. Her popularity is indeed on the rise, but her reach still covers less than a quarter of all music listeners.

Obviously, even a champion single can still leave people wondering, "Who is this?" So, what exactly has happened to this so-called "homogeneous culture"?

If you're not familiar with this term, "monoculture" ( monoculture ) refers to a set of shared cultural references: whether people like it or not, it's assumed that everyone knows about those songs, programs, and movies. (Recently, this term has taken on a second, almost opposite meaning: critics use it to describe how algorithms and artificial intelligence are homogenizing culture into a uniformity.)

In the past, it was easier for people to share these cultural references, as a large portion of the content watched and listened to came from a few major television networks, radio stations, and MTV. The “gatekeepers” of the industry decided what would reach the general public. You didn’t have to be a fan to know what everyone was talking about.

This shared audience has never been “everyone,” and it began to fragment even before the emergence of TikTok. However, personalized information flows have pushed this division even further: what you are able to see increasingly depends on what the system believes “you” specifically want to see.

Taking TikTok as an example, it has always emphasized the personalization of the “For You” information flow. As the company says, “There is no such thing as a unified For You information flow.”

Have you ever had this experience? You open social media, watch a clip from a Olivia Rodrigo concert, and then it feels like every five videos, there’s another similar one popping up.

TikTok indicates that it gathers signals based on the content you like and share, the people you follow, and whether you watch videos to the end. It also analyzes the videos themselves, including their titles, sounds, and tags. Even your language preferences, country settings, and device type are taken into consideration, although TikTok states that these factors have a lower weight. All this information helps the system determine what content is worth your attention for a few more seconds. TikTok also means that it strives to maintain diversity in the information stream; for example, it generally won't display two consecutive videos with the same voice or from the same creator.

In the United States, this system is currently overseen by a joint venture that receives support from Oracle. The joint venture took over the TikTok US business in January and stated that it will retrain the recommendation algorithms based on data from US users.

How does the feedback loop work?

If you continue to watch, the algorithm may continue to push more similar content. Researchers have already observed the actual operation of this feedback loop.

A study published in EPJ Data Science in February this year used automated accounts set with different interest preferences to test the “For You” information flow of TikTok. The study found that TikTok began to recommend more and more videos that matched these interests, and this reinforcement usually started to become apparent within the first 200 videos viewed. While the information flow still retained a certain degree of diversity, the stronger a user’s interests were amplified, the fewer different tags they were exposed to. (The researchers conducted the experiment in 2024, which was before the change in ownership of TikTok in the United States.)

This helps to explain why you might feel that something is “sweeping the internet,” while someone next to you, like Joe, sees a completely different stream of information and might even wonder what you’re talking about at all.

You can also further narrow down your choices on your own. Platforms like TikTok provide some tools, such as the “Manage Topics” settings, that allow you to tell the system which topics you want to see more of and which you want to see less of.

Observe you from all angles.

Clues used to personalize your information flow, which may even come from outside of the application.

Meta announced in June that it would use information that other companies had already shared with them – such as people's purchase records from other websites – to personalize users' information streams, and not just display advertisements. An example given by the company is: if you buy a tent online, you might start seeing more content related to camping. Users can manage this feature through a setting called "Activities from Other Companies."

Whatever you look at, whatever you buy – all of these can be included in the calculation process, shaping that little piece of internet space that belongs solely to you. A space custom-made just for you.

Not everyone likes it. The author of The New Yorker, Kyle Chayka, has raised quite a few criticisms about this convenience and even wrote an entire book about it: Filterworld: How Algorithms Flattened Culture.

In an interview with the NPR program “Fresh Air” in 2024, he said: “These digital platforms and information flows, to some extent, promise a great common experience... But I believe that they are actually atomizing our experiences, because we can never know what others see in their own information flows.”

He believes that this weakens a part of the pleasure that art brings and also makes it more difficult for people to share a common sense of excitement about the same things.

Smarter algorithms might end up keeping us in our own separate “bubbles.” But if there’s something you’re not interested in at all, would you really want it to appear in your information stream just for the sake of sharing a cultural experience? And if it never reaches you, how would you even know whether you’d like it or not?