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Understanding Decentralized Social Feed Curation on Mastodon

  • Yuhan Liu
  • , Emmy Song
  • , Owen Xingjian Zhang
  • , Jewel Merriman
  • , Lei Zhang
  • , Andrés Monroy-Hernández

Research output: Contribution to journalArticlepeer-review

Abstract

Amid rising concerns over moderation, algorithmic control, and platform governance on centralized social media, people are increasingly turning to decentralized alternatives like Mastodon to regain control over their feeds. This shift offers new opportunities to understand how people perceive and curate their feeds. We conducted a two-part study with 21 Mastodon users: first, interviews exploring how they perceive and manage their feeds; and second, a design probe study using BRAIDS.SOCIAL, a web-based feed curation prototype informed by the first part of our initial findings. We learned how seamful design can increase people's trust in algorithmic curation, and surfaced trade-offs people navigate between machine learning-based and rule-based filtering approaches. We also identify a core design tension in decentralized platforms: whether to support personalization through new applications or extensions layered atop existing ones.

Original languageEnglish (US)
Article numberCSCW507
JournalProceedings of the ACM on Human-Computer Interaction
Volume9
Issue number7
DOIs
StatePublished - Oct 16 2025
Externally publishedYes

All Science Journal Classification (ASJC) codes

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

Keywords

  • algorithmic feed
  • fediverse
  • mastodon
  • social media

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