Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study

TL;DR

This study investigates how users migrated from Twitter to Mastodon following Twitter's ownership change, focusing on migration patterns, the influence of platform architectures on user behavior, and factors predicting sustained Mastodon use. It combines account-mapping algorithms, large-scale activity and network collection, occupational coding via SOC, and semantic analyses (BERTopic and DeBERTa ABSA) to reveal phase-based migration, configuration-dependent behavior, and retention factors. Key findings show that migration occurred in protesting, adaptation, and current phases, with attention shifts often returning to Twitter; Twitter users exhibit higher activity and occupational inequality, while Mastodon fosters diverse interactions and niche content, with interaction diversity and fandom migration predicting residency. The results offer actionable insights for platform designers seeking to support sustainable migration by emphasizing cross-server engagement and community-centric dynamics over sheer volume of responses.

Abstract

A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are, how different platforms impact user behaviors, and how migrated users settle in the migration process. In this study, we elaborate on how we investigate these questions by collecting data over 10,000 users who migrated from Twitter to Mastodon within the first ten weeks following the ownership change of Twitter. Our research is structured in three primary steps. First, we develop algorithms to extract and analyze migration patterns. Second, by leveraging behavioral analysis, we examine the distinct architectures of Twitter and Mastodon to learn how user behaviors correspond with the characteristics of each platform. Last, we determine how particular behavioral factors influence users to stay on Mastodon. We share our findings of user migration, insights, and lessons learned from the user behavior study.

Figures (11)

• Figure 1: The distinct platform architectures of (a) Twitter, which is a centralized platform, and (b) Mastodon, which employs a decentralized platform with a federated network.
• Figure 2: The process of migration, while still maintaining the previous account and shifting attention between platforms.
• Figure 3: The process of data collection, including mapping accounts, using both Twitter and Mastodon as resources.
• Figure 4: The pie chart illustrates the distribution of the distribution of nine major groups, each accompanied by corresponding tags. The major groups are based on the first digit of the UK's Standard Occupational Classification (SOC 2010) code, which has been assigned to each user. Further descriptions regarding nine major groups in the UK's SOC 2010 are provided in Appendix.
• Figure 5: Trends in daily active users tagged with major events. Twitter-only (blue) and Mastodon-only (orange) lines indicate the number of users active exclusively on one platform, while Twitter & Mastodon (green) represents users active on both platforms. The $x$-axis denotes a particular date $t_j$, when we assess whether a user was active within the interval of $\delta$ = 1 day. Red dashed lines highlight the key moments, where the temporal shifts overlap among the three trends (blue, orange, and green).