Threads Experiments with Custom Tag Tool to Hand Feed Algorithm Control to Users

Meta’s Threads is on the verge of turning feed control upside down. The social platform, fresh from its whirlwind rise in the microblogging arena, is quietly trialing a new tool that lets users steer its all-powerful algorithm with custom tags, reshaping how content is discovered, followed, and experienced in real time.

Rethinking the Algorithmic Feed

For years, users have watched their feeds morph according to mysterious, ever-changing algorithms. Now, Threads is handing over the reins. Selected users have spotted a “Tag” tool that allows them to manually label Topics or Interests. These tags work as personalized filters, nudging the platform’s discovery system to curate feeds around user-defined themes, not just hashtags but broader concepts, moods, or areas of interest.

From Passive Scrolling to Active Curation

Instead of being passive scrollers, users on Threads will soon wield agency. By tagging the algorithm itself, they can push up what matters most, whether it’s “AI startups,” “indie authors,” or “late night memes.” Threads’ feed promises to respond instantly, dialing up or down topics on demand. This kind of user-steered curation edges closer to customizable newsrooms, sidestepping the old frustrations of irrelevant or repetitive posts.

What Sets Threads’ Tag Tool Apart?

Unlike features on rival platforms that lean on trending hashtags or rigid lists, this experiment opens the door for a genuinely flexible feed, one that can be reconfigured in minutes. The tag system apparently works beyond post content, tapping into the underlying interests marked by the user, layering in a new level of control rarely offered by large social platforms.

Why This Matters Now

Threads is clearly betting that transparency and user empowerment are the next big differentiators in social media. With user trust an ongoing battle and “algorithm fatigue” hitting mainstream audiences, this feature signals a shift. Platforms want users to help train the algorithms themselves. If the test proves successful, expect to see similar experiments ripple through the industry, putting power not just in users’ hands but in their tags.