3 Newcomers
3.1 Introduction
Following Twitter’s 2022 acquisition, Mastodon—an open-source, decentralized social network and microblogging community—saw an increase in activity and attention as a potential Twitter alternative (He et al. 2023; La Cava, Aiello, and Tagarelli 2023). While millions of people set up new accounts and significantly increased the size of the network, many newcomers found the process confusing and many accounts did not remain active. Unlike centralized social media platforms, Mastodon is a network of independent servers with their own rules and norms (Nicholson, Keegan, and Fiesler 2023). Each server can communicate with each other using the shared ActivityPub protocols and accounts can move between Mastodon servers, but the local experience can vary widely from server to server.
Although attracting and retaining newcomers is a key challenge for online communities (Kraut, Resnick, and Kiesler 2011, 182), Mastodon’s onboarding process has not always been straightforward. Variation among servers can also present a challenge for newcomers who may not even be aware of the specific rules, norms, or general topics of interest on the server they are joining (Diaz 2022). Various guides and resources for people trying to join Mastodon offered mixed advice on choosing a server. Some suggest that the most important thing is to simply join any server and work from there (Krasnoff 2022; Silberling 2023), while others have created tools and guides to help people find potential servers of interest by size and location(thekinrarMastodonInstances2017?; King 2024).
Mastodon’s decentralized design has long been in tension with the disproportionate popularity of a small set of large, general-topic servers within the system (Raman et al. 2019). Analysing the activity of new accounts that join the network, we find that users who sign up on such servers are less likely to remain active after 91 days. We also find that many users who move accounts tend to gravitate toward smaller, more niche servers over time, suggesting that established users may also find additional utility from such servers.
In response to these findings, we propose a potential way to create server and tag recommendations on Mastodon. This recommendation system could both help newcomers find servers that match their interests and help established accounts discover “neighborhoods” of related servers to enable further discovery.
3.2 Background
3.2.1 Empirical Setting
The Fediverse is a set of decentralized online social networks which interoperate using shared protocols like ActivityPub. Mastodon is a software program used by many Fediverse servers and offers a user experience similar to the Tweetdeck client for Twitter. It was first created in late 2016 and saw a surge in interest in 2022 during and after Elon Musk’s Twitter acquisition.
Mastodon features three kinds of timelines. The primary timeline is a “home” timeline which shows all posts from accounts followed by the user. Mastodon also supports a “local” timeline which shows all public posts from the local server and a “federated” timeline which includes all posts from users followed by other users on their server. The local timeline is unique to each server and can be used to discover new accounts and posts from the local community. On larger servers, this timeline can be unwieldy; however, on smaller servers, this presents the opportunity to discover new posts and users of potential interest.
Discovery has been challenging on Mastodon. Text search, for instance, was impossible on most servers until support for this feature was added on an optional, opt-in basis using Elasticsearch in late 2023 (Rochko 2023b). Recommendation systems are currently a somewhat novel problem in the context of decentralized online social networks. Trienes, Cano, and Hiemstra (2018) developed a recommendation system for finding new accounts to follow on the Fediverse which used collaborative filtering based on BM25 in an early example of a content discovery system on Mastodon.
Individual Mastodon servers can have an effect on the end experience of users. For example, some servers may choose to federate with some servers but not others, altering the topology of the Fediverse network for their users. At the same time, accounts can only map to one specific server. Because of Mastodon’s data portability, users can move their accounts freely between servers while retaining their followers, though their post history remains with their original account.
3.2.2 The Mastodon Migrations
Mastodon saw a surge in interest in 2022 and 2023, particularly after Elon Musk’s Twitter acquisition. In particular, four events of interests drove measurable increases in new users to the network: the announcement of the acquisition (April 14, 2022), the closing of the acquisition (October 27, 2022), a day when Twitter suspended a number of prominent journalists (December 15, 2022), and a day when Twitter experienced an outage and started rate limiting accounts (July 1, 2023). Many Twitter accounts announced they were setting up Mastodon accounts and linked their new accounts to their followers, often using tags like #TwitterMigration(He et al. 2023) and driving interest in Mastodon in a process La Cava, Aiello, and Tagarelli (2023) found consistent with social influence theory.
Some media outlets have framed reports on Mastodon (Hoover 2023) through what Zulli, Liu, and Gehl (2020) calls the “Killer Hype Cycle”, whereby the media finds a new alternative social media platform, declares it a potential killer of some established platform, and later calls it a failure if it does not displace the existing platform. Such framing fails to take systems like the Fediverse seriously for their own merits: completely replacing existing commercial systems is not the only way to measure success, nor does it account for the real value the Fediverse provides for its millions of active users.
Mastodon’s approach to onboarding has also changed over time. In much of 2020 and early 2021, the Mastodon developers closed sign-ups to their flagship server and linked to an alternative server, which saw increased sign-ups during this period. They also linked to a list of servers on the “Join Mastodon” webpage (Mastodon gGmbH n.d.), where all servers are pre-approved and follow the Mastodon Server Covenant which guarantees certain content moderation standards and data protections. Starting in 2023, the Mastodon developers shifted toward making the flagship server the default when people sign up on the official Mastodon Android and iOS apps (Rochko 2023a; Roth 2023).
3.2.3 Newcomers in Online Communities
Onboarding newcomers is an important part of the life cycle of online communities. Any community can expect a certain amount of turnover, and so it is important for the long-term health and longevity of the community to be able to bring in new members (Kraut, Resnick, and Kiesler 2011, 182). However, the process of onboarding newcomers is not always straightforward.
The series of migrations of new users into Mastodon in many ways reflect folk stories of “Eternal Septembers” on previous communication networks, where a large influx of newcomers challenged the existing norms (Driscoll 2023; Kiene, Monroy-Hernández, and Hill 2016). Many Mastodon servers do have specific norms which people coming from Twitter may find confusing, such as local norms around content warnings (Nicholson, Keegan, and Fiesler 2023). Variation among servers can also present a challenge for newcomers who may not even be aware of the specific rules, norms, or general topics of interest on the server they are joining (Diaz 2022). Mastodon servers open to new accounts must thus be both accommodating to newcomers while at the same ensuring the propagation of their norms and culture, either through social norms or through technical means.
3.2.4 Recommendation Systems and Collaborative Filtering
Recommender systems help people filter information to find resources relevant to some need (Ricci, Roḳaḥ, and Shapira 2022). The development of these systems as an area of formal study harkens back to information retrieval (e.g. Salton and McGill (1987)) and foundational works imagining the role of computing in human decision-making (e.g. Bush (1945)). Early work on these systems produced more effective ways of filtering and sorting documents in searches such as the probabilistic models that motivated the creation of the okapi (BM25) relevance function (Robertson and Zaragoza 2009). Many contemporary recommendation systems use collaborative filtering, a technique which produces new recommendations for items based on the preferences of a collection of similar users (Koren, Rendle, and Bell 2022).
Collaborative filtering systems build on top of a user-item-rating (\(U-I-r\)) model where there is a set of users who each provide ratings for a set of items. The system then uses the ratings from other users to predict the ratings of a user for an item they have not yet rated and uses these predictions to create a ordered list of the best recommendations for the user’s needs (Ekstrand, Riedl, and Konstan 2011, 86–87). Collaborative filtering recommender systems typically produce better results as the number of users and items in the system increases; however, they must also deal with the “cold start” problem, where limited data makes recommendations unviable (Lam et al. 2008). The cold start problem has three possible facets: boostrapping new communities, dealing with new items, and handling new users (Schafer et al. 2007, 311–12). In each case, limited data on the entity makes it impossible to find similar entities without some way of building a profile. Further, uncorrected collaborative filtering techniques often also produce a bias where more broadly popular items receive more recommendations than more obscure but possibly more relevant items (Zhu et al. 2021). Research on collaborative filtering has also shown that the quality of recommendations can be improved by using a combination of user-based and item-based collaborative filtering (Sarwar et al. 2001).
Although all forms of collaborative filtering use some combination of users and items, there are two main approaches to collaborative filtering: memory-based and model-based. Memory-based approaches use the entire user-item matrix to make recommendations, while model-based approaches use a reduced form of the matrix to make recommendations. This is particularly useful because the matrix of items and users tends to be extremely sparse, e.g. in a movie recommendor system, most people have not seen most of the movies in the database. Singular value decomposition (SVD) is one such dimension reduction technique which transforms a \(m \times n\) matrix \(M\) into the form \(M = U \Sigma V^{T}\) (Paterek 2007). SVD is particularly useful for recommendation systems because it can be used to find the latent factors which underlie the user-item matrix and use these factors to make recommendations.
While researchers in the recommendation system space often focus on ways to design the system to produce good results mathematically, human-computer interaction researchers also consider various human factors which contribute to the overall system. Crucially, McNee et al. argued “being accurate is not enough”: user-centric evaluations, which consider multiple aspects of the user experience, are necessary to evaluate the full system. HCI researchers have also contributed pioneering recommender systems in practice. For example, GroupLens researchers Resnick et al. (1994) created a collaborative filtering systems for Usenet and later produced advancements toward system evaluation and explaination of movie recommendations (Herlocker et al. 2004; Herlocker, Konstan, and Riedl 2000). Cosley et al. (2007) created a system to match people with tasks on Wikipedia to encourage more editing. This prior work shows that recommender systems can be used to help users find relevant information in a variety of contexts.
3.2.5 Evaluation of Recommendation Systems
Evaluating recommender systems can be tricky because a measure of good performance must take into account various dimensions(Zangerle and Bauer 2022). A measure of accuracy must be paired with a question of “accuracy toward what?” Explainability requires a transparent means of showing the user why a certain item was recommended.
It is often important to both start with an end goal in mind and to keep evaluation integrated throughout the entire process of creating a recommender systems, from conceptualization to optimization. There are several considerations to keep in mind such as the trade-off between optimizing suggestions and the risks of over-fitting. For example, a system designed to create suggestions with the highest propensity that the user will like the recommendations may struggle with a reduced diversity of its suggestions.
Recommender systems can be evaluated using three board categories of techniques: offline, online, and user studies. Offline evaluation uses pre-collected data and a measure to describe the performance of the system, assuming there is insufficient relevance to the difference in time between when the data was collected and the present moment. Online evaluation uses a deployed, live system, e.g. A/B testing. In this case, the user is often unaware of the experiment. In contrast, user studies involve subjects which are aware they are being studied.
3.3 Data
Mastodon has an extensive API which allows for the collection of public posts and account information. We collected data from the public timelines of Mastodon servers using the Mastodon API with a crawler which runs once per day. We also collected account information from the opt-in public profile directories on these servers.
3.3.1 Initial Findings
3.3.1.1 Account Survival
Our initial findings suggest that servers do matter for newcomers on Mastodon. Figure 3.2 uses a Kaplan–Meier estimator to show that accounts on the largest Mastodon servers featured on the Join Mastodon website are less likely to remain active compared to accounts on smaller servesr featured on Join Mastodon. Further, Table 3.1 uses a Cox Proportional Hazard Model with mixed effects to suggest that small servers are significantly better at retaining new accounts and that general servers are less likely to retain new accounts; being featured on the Join Mastodon website appears to have no significant effect.
| Term | Estimate | Low | High | p-value |
|---|---|---|---|---|
| Join Mastodon | 0.115 | 0.972 | 1.296 | 0.117 |
| General Servers | 0.385 | 1.071 | 2.015 | 0.017 |
| Small Server | -0.245 | 0.664 | 0.922 | 0.003 |
3.3.1.2 Moved Accounts
| Model A | Model B | |||
|---|---|---|---|---|
| Coef. | Std.Error | Coef. | Std.Error | |
| (Sum) | -9.529 | ***0.188 | -10.268 | ***0.718 |
| nonzero | -3.577 | ***0.083 | -2.861 | ***0.254 |
| Smaller server | 0.709 | ***0.032 | 0.629 | ***0.082 |
| Server size (outgoing) | 0.686 | ***0.013 | 0.655 | ***0.042 |
| Open registrations (incoming) | 0.168 | ***0.046 | -0.250 | 0.186 |
| Languages match | 0.044 | 0.065 | 0.589 | 0.392 |
To corroborate these findings, we also looked at accounts that moved from one server to another. We find that accounts are more likely to move from larger servers to smaller servers.