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The Failed Migration of Academic Twitter

Xinyu Wang, Sai Koneru, Sarah Rajtmajer

TL;DR

The movement lost momentum after an initial surge of enthusiasm as most users did not maintain their activity levels, and those who did faced lower levels of engagement, highlighting the need for focus on community building for long-term user engagement.

Abstract

Following changes in Twitter's ownership and subsequent changes to content moderation policies, many in academia looked to move their discourse elsewhere and migration to Mastodon was pursued by some. Our study looks at the dynamics of this migration. Utilizing publicly available user account data, we track the posting activity of academics on Mastodon over a one year period. We also gathered follower-followee relationships to map internal networks, finding that the subset of academics who engaged in migration were well-connected. However, this strong internal connectivity was insufficient to prevent users from returning to Twitter/X. Our analyses reveal significant challenges sustaining user engagement on Mastodon due to its decentralized structure as well as competition from other platforms such as Bluesky and Threads. The movement lost momentum after an initial surge of enthusiasm where the main network was fully established as most users did not maintain their activity levels, and those who did faced lower levels of engagement. Our findings highlight the challenges involved in transitioning professional communities to decentralized platforms, emphasizing the need for focus on community building for long-term user engagement.

The Failed Migration of Academic Twitter

TL;DR

The movement lost momentum after an initial surge of enthusiasm as most users did not maintain their activity levels, and those who did faced lower levels of engagement, highlighting the need for focus on community building for long-term user engagement.

Abstract

Following changes in Twitter's ownership and subsequent changes to content moderation policies, many in academia looked to move their discourse elsewhere and migration to Mastodon was pursued by some. Our study looks at the dynamics of this migration. Utilizing publicly available user account data, we track the posting activity of academics on Mastodon over a one year period. We also gathered follower-followee relationships to map internal networks, finding that the subset of academics who engaged in migration were well-connected. However, this strong internal connectivity was insufficient to prevent users from returning to Twitter/X. Our analyses reveal significant challenges sustaining user engagement on Mastodon due to its decentralized structure as well as competition from other platforms such as Bluesky and Threads. The movement lost momentum after an initial surge of enthusiasm where the main network was fully established as most users did not maintain their activity levels, and those who did faced lower levels of engagement. Our findings highlight the challenges involved in transitioning professional communities to decentralized platforms, emphasizing the need for focus on community building for long-term user engagement.
Paper Structure (20 sections, 11 figures, 6 tables)

This paper contains 20 sections, 11 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Characteristics of Mastodon
  4. Cross-platform migration
  5. Data
  6. Results
  7. Longitudinal Analysis
  8. Attrition of academic accounts on Mastodon
  9. Engagement with academic accounts on Mastodon
  10. Inactive users.
  11. Field-specific activity differences among academic accounts on Mastodon
  12. Mastodon Network Analyses
  13. Internal Network Aggregated by Field
  14. Internal Networks aggregated by Server
  15. Case study: Mastodon Community of Information Security
  16. ...and 5 more sections

Figures (11)

  • Figure 1: Waterfall plot showing number of monthly active users (active=at least one post per month).
  • Figure 2: Average public metrics and statuses count for active users.
  • Figure 3: internal field network (size: number of scholars in each field; edge weights: frequency of connections; edge color: same as the source).
  • Figure 4: internal server network (removed isolated servers, red are servers with more than 100 scholars).
  • Figure 5: internal follower-followee network for Information Security community (removed isolates).
  • ...and 6 more figures