A Herd of Young Mastodonts: the User-Centered Footprints of Newcomers After Twitter Acquisition

TL;DR

This study analyzes Mastodon after the Twitter/X acquisition to understand how newcomers form structured social ties using ego-network analysis. It builds directed ego graphs from timelines via a snowball data collection strategy and extracts social circles with Meanshift clustering, highlighting activity before and after acquisition. The findings show Mastodon ego networks align with Dunbar’s model, exhibiting four to five circles and a scaling ratio near three, with a notably large outer layer indicative of a developing, 'young' network. The work positions Mastodon as an open, data-rich platform for studying human social behavior and diffusion dynamics in decentralized OSNs, with implications for research and potential comparisons across Fediverse platforms.

Abstract

The tremendous success of major Online Social Networks (OSNs) platforms has raised increasing concerns about negative phenomena, such as mass control, fake news, and echo chambers. In addition, the increasingly strict control over users' data by platform owners questions their trustworthiness as open interaction tools. These trends and, notably, the recent drastic change in X (formerly Twitter) policies and data accessibility through public APIs, have fuelled significant migration of users towards Fediverse platforms (primarily Mastodon). In this work, we provide an initial analysis of the microscopic properties of Mastodon users' social structures. Specifically, according to the Ego network model, we analyse interaction patterns between a large set of users (egos) and the other users they interact with (alters) to characterise the properties of those users' ego networks. As was observed previously in other OSNs, we found a quite regular structure compatible with the reference Dunbar's Ego Network model. Quite interestingly, our results show clear signs of ego network formation during the initial diffusion of a social networking tool, coherent with the recent surge of Mastodon activity. Therefore, our analysis motivates the use of Mastodon as an open "big data microscope" to characterise human social behaviour, making it a prime candidate to replace those OSN platforms that, unfortunately, cannot be used anymore for this purpose.

Figures (7)

• Figure 1: Layered structure of human ego networks
• Figure 2: Overview of the user activity over time. On the left (a), the average daily activity (measured as the average number of daily toots) per user. On the right (b), we show: the number of users that have posted once by date $x$ vs the number of users (which we call active) that will continue posting after date $x$ (b, top panel), number of users engaging in directed vs undirected toots for each day (b, middle panel), ratios between the metrics in the top and middle plots (b, bottom panel).
• Figure 3: Interactions between different categories of users.
• Figure 4: User activity vs number of alters
• Figure 5: Activity post-acquisition. On the $x$-axis, the lifespan of users (defined as the number of days since the first toot). On the $y$-axes, the plot shows: the number of users that have reached that lifespan (top), the number of users that have reached that lifespan and have continued posting directed/undirected toots (middle), the average number of daily toots split per type (bottom).