# 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 \[1] Hakaba Hitoyo, 脱中央集権のためのデザイン: セレブのためのインターネットを99 %の手に取り戻す,\ \[2] Hakaba Hitoyo, マストドンのユーザーレコメンデーションがブームに？,\ \[3] Hakaba Hitoyo, マストドンのユーザーレコメンデーションについて追記,\ \[4] User Local, Inc., Mastodon Ranking,\ \[5] Osa, Recommended Followers,\ \[6] Cucmberium, Mastodonおすすめユーザー検索,\ \[7] Takumi, マストドン (Mastodon) ユーザなら必ずフォローしたい！ アカウント一覧,\ \[8] Gargron, Public profile endorsements (accounts picked by profile owner) #8146,\ \[9] Hakaba Hitoyo, Endorsement is some kind of worship or flattery,\ \[10] \[11] pixiv Inc., Pawoo,\ \[12] \[13] Gargron, Add profile directory to web UI #11688,\ \[14] Gargron, Re-add follow recommendations API #7918,\ \[15] Hakaba Hitoyo, ユーザーレコメンデーションがマストドン本体に導入された: フェアネスの評価は「現状維持」,\ \[16] Kirino Minato, Tootsearch,\ \[17] User Local, Inc., マストドンリアルタイム検索,\ \[18] マストドン検索ポータル,\ \[19] Osa, マストドン検索 by google,\ \[20] Hakaba Hitoyo, User Matching for Fediverse,\ \[21] lain, Pleroma,\ \[22] Neetshin, Niklas Poslovski, Halcyon,\ \[23] Hakaba Hitoyo, Newcomers in Fediverse,\ \[24] Hakaba Hitoyo, User Recommendation Systems for Mastodon and their Fairness,\