Understanding Decentralized Social Feed Curation on Mastodon

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.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757688
Primary Topic
Privacy, Security, and Data Protection
Type
article
Field-Weighted Citation Impact
0.00
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article

Understanding Decentralized Social Feed Curation on Mastodon

Andrés Monroy‐Hernández, Lei Zhang, Owen Xingjian Zhang, Yuhan Liu et al.
Proceedings of the ACM on Human-Computer Interaction
Privacy, Security, and Data Protection
article

Understanding Decentralized Social Feed Curation on Mastodon

Andrés Monroy‐Hernández, Lei Zhang, Owen Xingjian Zhang, Yuhan Liu, Jewel Merriman, Emmy Song
article en

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.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
Princeton University (US)
Openalex Percentile: Top 61%
Privacy, Security, and Data Protection
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