Case study: Fairness of the user discovery methods for Mastodon
Motivation
The presence of users is oligopolized by few celebrities in the user-generated content platforms.
Decentralization of users is expected.
User recommendation systems for Mastodon
Lists of recommended users
User Matching for Fedierse (Vaginaplant's)
Mastodon Recommended User Search (Cucmberium's)
Recommended users
Similar users
Recommended Followers (Osa's)
Potential friendship (Mastodon's official/embedded)
Who to follow panels
Pawoo
Pleroma
Halcyon
Misskey
Other user discovery methods
Lists of users
By their number of followers
By their number of boosts
Newcomers
Recently posted
Manual
Public profile endorsements (Mastodon's official/embedded)
Timelines
Home
Federated
Local
Search engines
Summary of fairness and unfairness
↑ Fair
Newcomers
User Matching for Fediverse (Vaginaplant's)
Similar users (Cucmberium's)
Search engines
Potential friendship (Mastodon's official/embedded)
Local timeline
Federated timeline
Home timeline
Recently posted
Pawoo's who to follow panel
Misskey's who to follow panel
List of users by their number of boosts
Public profile endorsements (Mastodon's official/embedded)
Manual guidebooks
Recommended users (Cucmberium's)
Recommended Followers (Osa's)
List of users by their number of followers
↓ Unfair
Case 1: List of users by their number of followers
Implementation
Mastodon Ranking [4] by User Local, Inc.
Specification
Ordered by the number of followers.
Unfairness
Oldies effect: Once we had followed an user, we rarely unfollow the one, even if the user becomes not attractive anymore.
Positive feedback: If we follow the users in this kind of list, it directly accelerates itself.
Shrink-wrap effect: The list of users shows screen names, bios, and avatars. No content is shown apparently. We are attracted by the famous names and aesthetic avatars.
Fairness
No power user effect: By definition.
Summary
One of the most unfair user discovery methods ever.
Case 2: Recommended Followers
Implementation
Recommended Followers [5] by Osa
Specification
When you use this recommendation engine, it shows the users who are followed by the users who you follow.
Unfairness
The same oldies effect, positive feedback, and shrink-wrap effect as the list of users by their number of followers.
Recommends the celebrities in the small society.
Fairness
No power user effect: By definition.
Summary
One of the most unfair user discovery methods ever.
Case 3: Recommended users in Mastodon Recommended User Search
Implementation
Mastodon Recommended User Search [6] by Cucmberium
Specification
The users who your similar users follow.
Unfairness
The same oldies effect, positive feedback, and shrink-wrap effect as the list of users by their number of followers.
Fairness
No power user effect: By definition.
Summary
One of the most unfair user discovery methods ever.
Case 4: Manual guidebooks
Implementation
Mastodon Famous Accounts [7] by Takumi
Specification
A blog post which is manually written.
Unfairness
Oldies effect: Manual guidebooks are rarely updated.
Shrink-wrap effect: The authors of the guidebooks are attracted by famous names or aesthetic avatars.
The authors of the guidebooks sometimes just follow some existing authority.
Fairness
No positive feedback: Our action does not affect to the static documents.
No power user effect: By definition.
Summary
Super unfair user discovery.
Fair minded authors of the guidebooks are expected.
Case 5: Public profile endorsements
Implementation
Mastodon's officiel/embedded [8]
Specification
The user pins other users on one's profile.
Fairness and Unfairness
Almost same as the manual guidebooks.
Summary
Super unfair user discovery.
Endorsement is worship or flattery [9].
Case 6: List of users by their number of boosts
Implementation
Mastodon Ranking [4] by User Local, Inc.
Specification
Ordered by the number of boosts.
Unfairness
Oldies effect: The number of boosts is accumulated ever.
Positive feedback: If we follow the user who is boosted well, we boost such kind of user's posts more often.
Power user effect: More posts, more boosts.
Fairness
No shrink-wrap effect: We read the post before we boost it.
Summary
Super unfair user discovery.
Better than the user discovery methods which depend on the number of followers.
Case 7: Misskey's who to follow panel
Implementation
Misskey [10]
Specification
Posting in the last 7 days.
Local users.
Many followers.
Unfairness
The same positive feedback and shrink-wrap effect as the list of users by their number of followers.
Fairness
Less oldies effect: Requires posting in the last 7 days.
No power user effect: By definition.
Summary
Remarkably unfair user discovery.
Better than the oldies effected user discovery methods.
Case 8: Pawoo's who to follow panel
Implementation
Pawoo [11, 12]
Specification
Following in pixiv.
Popular (many favorited, many followers, many pictures) users.
Active (many recent posts) users.
Unfairness
Many favorited and many followers criteria causes oldies effect, positive feedback, and shrink-wrap effect.
Many pictures criteria causes oldies effect.
Many favorited and many pictures criteria cause power user effect.
Fairness
Less oldies effect: Requires recent posts.
Summary
Remarkably unfair user discovery.
Better than the oldies effected user discovery methods.
Case 9: Recently posted
Implementstion
Profile directory [13] (Mastodon's official/embedded, default)
Specification
List of the users who recently posted.
Unfairness
Power user effect: By definition.
shrink-wrap effect: By definition.
Fairness
No oldies effect: By defintion.
No positive feedback: By definition.
Summary
Still unfair user discovery.
Power user effect remarkably brings unfairness.
Case 10: Home timeline
Implementstion
Embedded
Specification
Following users' posts.
Following users' boosts.
Unfairness
Positive feedback: If you follow some users and boost their posts, it accelerates itself.
Power user effect: By definition.
Fairness
No oldies effect: By definition.
No shrink-wrap effect: By definition.
Summary
Still unfair user discovery.
Positive feedback remarkably brings unfairness.
Case 11: Federated timeline
Implementstion
Embedded
Specification
Local users' posts.
Posts by the remote users who are followed by the local users.
Unfairness
Positive feedback: If a local user follows a remote user, the other local users may follow the remote user via their federated timeline.
Power user effect: By definition.
Fairness
No oldies effect: By definition.
No shrink-wrap effect: By definition.
Summary
Still unfair user discovery.
Mixed feature of the home and local timelines.
Case 12: Local timeline
Implementstion
Embedded
Specification
Local users' posts
Unfairness
Power user effect: By definition.
Fairness
No oldies effect: By definition.
No positive feedback: By definition.
No shrink-wrap effect: By definition.
Summary
Still unfair user discovery.
Power user effect remarkably brings unfairness.
Case 13: Potential friendship
Implementstion
Mastodon's official/embedded [14]
Specification
When an user engages (replies, favourites, and boosts) other user, the potential friendship counter is bumped.
Fairness and Unfairness
Mixed feature of the home, fedarated, and local timelines [15].
Summary
Still unfair user discovery.
Case 14: Search engines
Implementation
Tootsearch [16]
Mastodon Realtime Search [17]
Mastodon Search Portal [18]
Mastodon Search by Google [19]
Specification
Search posts by keyword.
Unfairness
Power user effect: More posts, more hits.
Fairness
No oldies effect: By definition.
No positive feedback: By definition.
No shrink-wrap effect: By definition.
Summary
Still unfair user discovery.
Not suit for the user discovery use. Explicit keyword is mandatory.
Case 15: Similar users in Mastodon Recommended User Search
Implementation
Mastodon Recommended User Search [6] by Cucmberium
Specification
The users who follow who you follow.
Unfairness
Shrink-wrap effect: By definition.
Fairness
No oldies effect: By definition.
No positive feedback: By definition.
No power user effect: By definition.
Summary
Neutral for fairness and unfairness.
Small cliques or clusters are formed.
Case 16: User Matching for Fediverse
Implementation
User Matching for Fediverse [20] by Vaginaplant
Who to follow panel in Pleroma [21]
Who to follow panel in Halcyon [22]
Specification
The users who have the vocabulary which similar to you.
Vocabulary is abstracted from one's screen name, bio, and posts.
Users who have too much followers (500 by default) are omitted.
Unfairness
Shrink-wrap effect: By definition.
Fairness
No oldies effect: By definition.
Reverse positive feedback: Users who have too much followers are omitted.
No power user effect: By definition.
Summary
Fair user discovery.
Case 17: Newcomers
Implementation
Profile directory [13] (Mastodon's official/embedded)
Newcomers in Fediverse [23] by Vaginaplant
Specification
List of newcomers.
Unfairness
Shrink-wrap effect: By definition.
Fairness
Reverse oldies effect: By definition.
No positive feedback: By definition.
No power user effect: By definition.
Summary
Fair user discovery.
References
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