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.
