Social Media Apps Give Users More Say Over What Appears in Their Feeds
Sandego.net – Pregnancy videos, nursery checklists and dramatic birth stories can quickly take over a social media feed once an app detects that someone is expecting a child. For one pregnant user, that stream became too much. She told Instagram she wanted less content about “pregnancy” and “birth,” while choosing to retain suggestions for “newborn parenting hacks.”
That kind of detailed input is becoming more common across major social platforms. After years of putting recommendation engines at the center of the scrolling experience, companies are introducing more tools that allow people to steer, limit or rebuild the content they receive.
Instagram and Facebook, both owned by Meta, have expanded options for adjusting recommendations. TikTok and YouTube have added comparable controls, while newer services such as Divine are also experimenting with customizable feeds organized around individual interests. The shift reflects a broader effort to combine algorithmic discovery with the sense of choice users had when feeds were primarily based on accounts they intentionally followed.
“What we’ve really tried to focus on is: how can we recreate the sense of agency that you had when you were controlling the accounts that you followed, but in a world that still gives you the benefits of recommendations,”
Instagram Vice President of Product Tessa Lyons said the goal is to preserve the ability to find new creators and subjects without leaving users feeling that the app has taken over their feed.
From passive signals to direct instructions
For much of the social media era, platforms inferred interests from behavior. Watching a video to the end, pausing on a post, liking a clip, searching for a subject or sharing content with a friend could all become signals that shaped future recommendations.
Those predictions can be useful, but they can also produce uncomfortable results. A person may watch a single video out of curiosity and then receive a long series of similar posts. Users have long complained about being pushed into irrelevant content loops, and some have raised concerns about exposure to upsetting or harmful material. Recommendation systems have faced especially intense scrutiny over the content they may direct toward younger audiences.
Newer artificial intelligence systems are allowing platforms to interpret more precise requests expressed in ordinary language. Instead of relying only on activity patterns, a user can increasingly tell an app what they want more of, what they want less of, or what topics they would rather avoid.
“We’re able to do something we’re never able to do before, which is actually enable people to use human language… to say, like, I want to see more of this topic or less of this topic,”
Lyons said. She added that platforms previously could not reliably determine the most relevant material even when someone named a topic as specific as “tennis.”
The development matters because feed controls are only valuable if the service can understand them. A vague “not interested” button may help reduce a particular post, but a topic-level setting gives users a way to express a broader preference without repeatedly correcting the algorithm one video at a time.
A response to growing frustration
The latest tools mark a notable change from the direction large social networks took in recent years. Many moved away from chronological timelines, which showed updates from followed accounts in time order, and toward recommendation-driven feeds designed to keep people discovering more content.
Platforms have argued that recommendations can surface posts and creators people might otherwise miss. Their systems draw on extensive data from activity on services and, in some cases, beyond them to anticipate which material will hold a person’s attention. That approach can support engagement, but it has also left many users feeling detached from the choices governing their online experience.
Tim Weninger, a computer science and engineering professor at the University of Notre Dame who researches AI and social media, said user impatience has increased as artificial intelligence plays a larger role in organizing online information. The reaction has developed alongside a wider public pushback against AI.
“People have gotten fed up with AI feeding them stuff,”
Weninger said.
“They miss the old days … They just want to be able to follow what they follow and see the latest updates.”
For everyday users, the question is not necessarily whether algorithms should disappear. Many people appreciate finding a recipe, hobby community, travel idea or new creator they would not have sought out on their own. The issue is whether that discovery comes with practical controls when recommendations become repetitive, intrusive or unhelpful.
How the newest controls work
Instagram reintroduced chronological viewing choices in 2022 through its “following” and “favorites” feeds. Those options limit posts to accounts selected by the user, although Instagram continues to open to its main recommended feed by default.
TikTok added another layer of personalization last June with “manage topics.” The feature lets users use sliders to influence the amount of content they see from broad categories, including fashion and beauty or health and fitness. It does not completely eliminate a category, but it gives TikTok a clearer indication of a person’s preferences.
The platform also introduced “smart keyword filtering.” When someone flags a keyword, TikTok can remove videos containing that term as well as posts using related language. The function may be particularly helpful for users who want to reduce a recurring subject without changing every other part of their feed.
Instagram later expanded the idea with “your algorithm,” a feature that displays the topics the app believes matter to an individual user. People can add subjects they want to encounter more often and identify subjects they prefer to avoid. Initially available only in the Reels section, the control now applies across Instagram and has recently been extended to Facebook.
These options do not mean that feeds have become fully manual. Algorithms still determine much of what appears on the largest platforms, and a user’s behavior continues to shape recommendations. But the new controls offer a more visible way to challenge those assumptions.
For people overwhelmed by a sudden flood of pregnancy content, fitness clips, political arguments or any other theme, the practical lesson is increasingly straightforward: feed preferences are no longer limited to likes, follows and silent scrolling. Users can now tell platforms more directly what they want their time online to look like.
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