"My finger hovered over my AirPodand stopped. I didn't want Spotify to get the wrong idea. I didn't want the all-knowing algorithmic DJ to think I disliked the song. The hesitation lasted less than a second, but it revealed something uncomfortable: I wasn't making the choice for me. I was making it for the version of me the algorithm believes in."
"I tailor how long I pause on Instagram Reelsnot because of how I feel about them, but because I don't want the platform to misclassify me. I consider avoiding edgier political videos I'm curious about because I don't want YouTube to drag me into a new ideological lane. I sometimes don't open messages because I know the app will reshuffle my entire social world based on that one tap."
"Friends admitted they do the same thing. Not dramaticallybut constantly. Micro-adjustments, all day long. A kind of self-policing. A low-level performance. Call it algorithmic discipline. We're not being watched by people. We're performing for machines. For years we were told about filter bubbles and echo chambers, as though we were passive victims of someone else's programming. But that's not what's happening anymore."
People modify everyday actions to influence algorithmic perceptions, performing for machine-driven feeds rather than personal preferences. Users hesitate to skip songs, pause differently on short videos, avoid politically edgy content, and delay opening messages to prevent reshaping recommendation profiles. These micro-adjustments amount to continuous self-policing and algorithmic discipline, producing low-level performances that stabilize user identities. Social networks no longer only impose filter bubbles; users internalize feed logic and build self-reinforcing cages that narrow exposure. The algorithm's incentives reward conformity and predictability, encouraging behaviors that preserve existing profiles instead of fostering genuine exploration.
Read at www.mediaite.com
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