"how advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation

Renkai Ma, Yubo Kou

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.

Original languageEnglish (US)
Article number429
JournalProceedings of the ACM on Human-Computer Interaction
Volume5
Issue numberCSCW2
DOIs
StatePublished - Oct 18 2021

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications

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