Not For You

Not For You

An automated confusion system for TikTok

What is Not For You?
Not For You is an artwork that presents an “automated confusion system” designed to mislead TikTok’s recommendation algorithm, reveal suppressed content, and steer users away from mimicry and conformity.
Merlin
Stats
Users: 66 ▼ -4
Version: 1.0.0 (Last updated: 2020-10-26)
Creation date: 2020-10-26
Risk impact: Low risk impact
Risk likelihood: Very low risk likelihood
Manifest version: 2
Permissions:
  • activeTab
  • storage
Size: 118.78K
URLs: Website
Stats date:

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Merlin
Summary

Not For You is an artwork that presents an “automated confusion system” designed to mislead TikTok’s video recommendation algorithm, making it possible to see how TikTok feels when it’s no longer made “For You.” The system navigates the site, clicking on videos and hashtags and users to find the nooks and crannies TikTok’s algorithm doesn’t show us, to reveal those videos its content moderators suppress, and to surface speech the company hopes to hide. Through its alternative personality-agnostic choices of what to like, who to follow, and which posts to share, Not For You should make the For You page less addictive, and hopefully steer users away from feeling like the best path to platform success is through mimicry and conformity. Perhaps most importantly—on the precipice of yet another critical election in the USA—Not For You aims to defuse the filter bubbles produced by algorithmic feeds and the risks such feeds pose for targeted disinformation and voter manipulation. Finally, the work stands in opposition to letting corporations opaquely decide what we see and when we see it, to their intentional crafting of addictive user interfaces, and to the extraction of profit from the residual data left behind by users. Ultimately, Not For You asks us to think about who most benefits from social media’s algorithmic feeds, and who is made most vulnerable.

Safety
Risk impact

Not For You is relatively safe to use as it requires very minimum permissions.

Risk likelihood

Not For You has earned a good reputation and can be trusted.

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