TikTok Users Flag Feed Glitch as Previously Viewed Content Resurfaces
Numerous accounts indicate recommended videos frequently stretch back several months, with certain clips originating from last summer or beyond, undermining the constant content discovery that has defined TikTok's core attraction.
"I keep seeing videos I've already liked or shared. It's boring and stressful," one subscriber posted in a viral message. A separate user questioned: "Every three videos, I recognize the content. Why am I seeing Halloween videos again?"
Comparable grievances have proliferated swiftly across the application, with multiple posts accumulating hundreds of thousands of impressions. Certain subscribers report diminished platform engagement due to the glitch, while others characterize their feeds as "completely disorganized."
A French digital media reporter identified the development as evidence of "major problems" affecting TikTok's recommendation infrastructure.
"My page only shows videos from months or even years ago. The feed I carefully curated no longer shows what I want to see," he remarked.
Online theories have multiplied regarding potential explanations, spanning technical malfunctions, internal experimentation, or modifications connected to government oversight and political threats.
TikTok's Chinese parent company ByteDance has engaged in negotiations to reorganize American operations facing potential prohibition, with a framework involving Oracle assisting in developing and protecting a restructured recommendation engine utilizing US subscriber information. This scenario fails to account for concurrent European disruptions.
Alternative explanations reference the EU's Digital Services Act (DSA), implemented in 2024, mandating enhanced accountability from major platforms while restricting dangerous or unlawful material. TikTok currently faces multiple probes under this framework.
The corporation suggests the occurrence might stem from standard algorithm experimentation. TikTok maintains its infrastructure occasionally resurfaces prior liked material assuming subscribers may desire repeated viewing.
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