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

Ujun Jeong1, Paras Sheth1, Anique Tahir1, Faisal Alatawi1, H. Russell
Bernard2, Huan Liu1

###### 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.

## Introduction

With the proliferation of social media platforms, users tend to be
increasingly mobile, moving between different platforms as their needs,
preferences, and interests change, a phenomenon known as online platform
migration (Zengyan, Yinping, and Lim 2009). The ownership change of Twitter on
October 27, 2022, created an opportunity to study platform migration as
numerous users migrated to Mastodon (Kupferschmidt 2022; He et al. 2023), a
microblogging platform with similar features to Twitter, such as “toots” and
“boosts” corresponding to “tweets” and “retweets,” but which operates on a
decentralized platform of self-hosted servers (Zignani, Gaito, and Rossi
2018).

Despite its decentralized architecture, Mastodon enables users to request
follows and share toots, boosts, or favorite requests across servers using the
shared Activity Pub protocol (Zignani, Gaito, and Rossi 2018). This
facilitates the formation of federated servers and interest-based communities,
each managed by distinct moderation policies, as depicted in Figure 1.
Mastodon is thus not a replica of Twitter; it provides a unique, community-
based user experience and interactions across various servers (La Cava, Greco,
and Tagarelli 2021). Such distinctive features made Mastodon an increasingly
popular choice for users seeking a new platform for migration.

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.

Online platform migration has been researched (Kumar, Zafarani, and Liu 2011;
Newell et al. 2016; Fiesler and Dym 2020; He et al. 2023). Observing the
exodus from Twitter, however, we are intrigued to understand user migration
patterns: (1) what follows the exodus, i.e., whether migration is persistent
or waning - this issue arises because users can maintain accounts on multiple
platforms, oscillating their focus between platforms until fully committing to
the new platform; (2) whether a platform architecture impacts the behaviors of
migrated users - exploring this connection may reveal the interplay between
platform differences, subsequent user behaviors post-migration, and varying
degrees of user engagement; and (3) what behavioral factors contribute to the
sustainability of platform migration is essential for a comprehensive grasp of
the dynamics of the migration process.

In this study, we investigate migration patterns by comparing behaviors of
users between Twitter and Mastodon, focusing on the dynamics of migrated users
switching their attention between the two platforms over time. To facilitate
this comparative study, we propose methods to map the accounts of over 10,000
individuals who migrated from Twitter to Mastodon and to determine users’
occupational backgrounds. Moreover, we collect data on user behaviors,
including user activities and network interactions on each platform during the
first ten weeks after Twitter’s leadership change.

Figure 2: The process of migration, while still maintaining the previous
account and shifting attention between platforms.

Our study is motivated by three research questions:

  * •

RQ1: What are the migration patterns observed between Twitter and Mastodon
after Twitter’s ownership change?

  * •

RQ2: What distinct user behaviors between Twitter and Mastodon arise due to
each platform’s architecture?

  * •

RQ3: What specific behavioral characteristics of migrated users influence
their decision to stay on Mastodon?

With respect to RQ1, we examine the evolving migration patterns with users’
active status and attention shifts between these platforms over time. Building
on this analysis, we correlate migration patterns with notable events, such as
Musk’s acquisition of Twitter, his promise of stepping down as a CEO, and so
on. Through comparison of discourse and sentiment with migration patterns, we
aim to uncover the potential motivations and how they affect migration
process.

In light of RQ2, we compare the behaviors of migrated users on Twitter and
Mastodon to understand the disparities between the two platforms. Especially,
our experiments include comparing the distribution of users’ occupations and
hashtags based on their popularity on each platform. This experiment aims to
serve as empirical evidence of how the design of each platform affects the
engagement of specific user groups more than others.

Regarding RQ3, we focus on the behavioral traits of users who stayed on
Mastodon despite notable events drawing attention back to Twitter. By
performing statistical analyses, we highlight associations between user
retention and various user behaviors, including some distinctive features
exclusive to Mastodon. Through this analysis, we shed light on Mastodon’s
unique appeal to its users and draw insights on certain behavioral factors
that promote sustainable migration.

Our main contributions are summarized as follows:

  * •

Following the platform policies, we crawl and curate a dataset of over 10,000
users who migrated from Twitter to Mastodon by developing an effective method
of mapping accounts between the two platforms.

  * •

We propose a novel framework for understanding the intricate dynamics of
migration patterns between Twitter and Mastodon, with a focus on the shift of
user attention between the two platforms. It enables us to study the interplay
among migration factors, the unique platform architectures, and the user
behaviors on each platform.

  * •

We present key behavioral factors that promote the sustainability of platform
migration to Mastodon, interesting insights for future study of platform
migration.

## Related Work

### Migration Theory and Platform Migration

Migration has long been a subject of study across social sciences, with the
push-pull theory being a key concept in this field (Levitt and Jaworsky 2007;
Lee 1966). The push-pull theory suggests that factors pushing people away from
a location and pulling them toward a new one drive migration decisions. This
concept can also be applied to online platform migration (Fiesler and Dym
2020; Gerhart and Koohikamali 2019; Newell et al. 2016). Newell et al. (2016)
studied the motivations through push-pull factors by surveying users who
migrated across subreddits during community unrest on Reddit, and found that
niche communities play an essential role in attracting users. Factors
including platform design, toxicity, moderation policy, presence of friends
and community can be motivations for migration (Fiesler and Dym 2020). Through
behavioral analysis, Kumar, Zafarani, and Liu (2011) found significant
differences between migrants and random users in three key areas: posting
activity, network activity, and Google search ranking. Lőrincz et al. (2017)
found that social capital, such as a high degree of connection and openness of
connection, can influence early abandonment of platform. He et al. (2023)
studied the recent surge in users migrating from Twitter to Mastodon. This
study presented several findings, including users’ tendency to join larger
servers, the higher number of imported Twitter followers on these larger
servers, and the fact that messages posted by migrated users had relatively
lower toxicity on Mastodon than on Twitter.

Mastodon’s decentralized architecture attracted many users from traditional
centralized platforms like Twitter and Facebook because of its several unique
characteristics (He et al. 2023; La Cava, Greco, and Tagarelli 2021). La Cava,
Greco, and Tagarelli (2021) found that connections in Mastodon are topic-
oriented, rather than popularity-driven due to the lack of a recommendation
system. Mastodon also emphasizes conversations and interactions over favorites
and reshares (La Cava, Greco, and Tagarelli 2022b). Despite Mastodon’s unique
appeals over traditional platforms, it faces numerous challenges inherent to
decentralized networks. Information consumers on Mastodon establish most
connections and broker information (La Cava, Greco, and Tagarelli 2022a). The
user-driven trend toward the centralization of Mastodon, primarily on a
handful of large servers, is also observed (Shaw 2020; Zignani, Gaito, and
Rossi 2018). Other issues, such as advertising revenue, handling moderation,
and the the availability of servers, pose challenges to operating Mastodon
(Anaobi et al. 2023; Zulli, Liu, and Gehl 2020; Raman et al. 2019).

Our study diverged from prior platform migration research by examining dynamic
migration patterns between Twitter and Mastodon over time. We focused on
users’ attention shifts between platforms and presented algorithms to infer
their migration motivations. Moreover, we analyzed the relation between
platform architecture and the behaviors of migrated users, identifying
behavioral factors that retain users on Mastodon. This holistic approach
deepens our understanding of migration patterns and sustainable platform
migration.

## Migration Types on Social Media

In social media and migration studies, two types of migration are identified
(Hou and Shiau 2020; Fiesler and Dym 2020; Kumar, Zafarani, and Liu 2011;
Levitt and Jaworsky 2007): (1) Permanent migration, where users transition to
a new platform, deactivate their original account, and exclusively engage on
the new platform; and (2) Attention migration, where users maintain presence
on both platforms but shift their focus toward one of them.

#### Permanent Migration.

Let $U_{p_{1}}$ represent the set of users on the platform $p_{1}$ and
$U_{p_{2}}$ represent the set of users on the platform $p_{2}$. A user $u$ is
considered to have permanently migrated from platform $p_{1}$ to platform
$p_{2}$ if two conditions are met: (1) the user $u$ was a member of $p_{1}$
before time $t$, and (2) the user $u$ is no longer a member of $p_{1}$ and is
a member of $p_{2}$ at time $t$ (i.e., $u\notin U_{p_{1}}$ and $u\in
U_{p_{2}}$).

Permanent migration may be a result of profile removal, deletion, or
suspension from the original platform.

#### Attention Migration.

For a user $u$ who is a member of both platforms (i.e., $u\in U_{p_{1}}$ and
$u\in U_{p_{2}}$) and active on both platforms at time $t_{i}$, attention
migration is said to occur between two distinct times $t_{i}$ and $t_{j}$
where $t_{j}>t_{i}$ if $u$ becomes inactive on platform $p_{1}$ while
remaining active on platform $p_{2}$ at time $t_{j}$. In this case, the user’s
attention is considered to have migrated from platform $p_{1}$ to platform
$p_{2}$ during the time interval $\delta=t_{j}-t_{i}$.

Attention migration could represent a short-term migration with the potential
to evolve into permanent migration over time. To ascertain a user’s active
status on a platform, we define an active user as:

#### Active User.

Given a social media platform $p$, a user $u\in U_{p}$, time $t_{j}>t_{i}$,
and interval $\delta=t_{j}-t_{i}$, $u$ is considered to be active on the
platform $p$ at time $t_{j}$, if the user has performed at least one activity
on the site since time $t_{i}$. Otherwise, the user is considered inactive.
User activities include various actions possible on the social media platform,
such as posting messages and resharing other users’ contents.

Our study recognizes that the permanent migration is not limited to the
transition from Twitter to Mastodon, as the reverse direction is also
possible. However, we focus on users who initiated migration from Twitter to
Mastodon to examine the impact of Twitter’s ownership change on migration. For
this specific objective, we excluded users who created Mastodon accounts
before creating their Twitter accounts.

Server Name | # User | # Migrant | Domain
---|---|---|---
mstdn.social | 176,621 | 1,965 | General
mastodon.world | 146,753 | 1,245 | General
mas.to | 148,995 | 2,180 | General
c.im | 61,854 | 397 | General
masto.ai | 64,908 | 452 | Technology
fosstodon.org | 52,993 | 1,230 | Technology
infosec.exchange | 45,532 | 2,087 | Technology
sfba.social | 36,972 | 325 | General
mindly.social | 33,855 | 179 | General
toot.community | 28,227 | 273 | General
Table 1: Top 10 Mastodon servers with most incoming users. We report the
number of migrants detected by our method.

## Data Collection

From October 24, 2022, to January 2, 2023, we identified a total of 10,333
migrated users who have accounts on both Twitter and Mastodon. We removed 56
users (0.5%) who had Mastodon accounts before Twitter accounts, along with 266
users (2.5%) whose accounts are inaccessible either on Twitter or Mastodon as
of January 2, 2023. This process left us 10,011 migrated users for our
analysis.

### Mapping Users across Platforms

Several services, such as Twitodon, aid in merging Twitter and Mastodon
accounts for users switching platforms but only support limited account
mappings. He et al. (2023) addressed this by analyzing tweets containing
Mastodon URLs, confirming matches only when Mastodon and Twitter usernames
were identical. In contrast, our approach allows for the flexible
identification of migrated users, even with nonidentical usernames, and
incorporates both Mastodon and Twitter as resources for account mapping.

Figure 3: The process of data collection, including mapping accounts, using
both Twitter and Mastodon as resources.

#### Mapping Twitter to Mastodon Accounts.

We leverage Twitter users’ display names in their profile to map their
Mastodon accounts, as these users often disclose their Mastodon usernames and
associated Mastodon servers within their display names. We first search for
users with the name of any Mastodon server listed in Table 1, and we extract
the Mastodon usernames from the display names using a regular expression that
identifies the word following the @ symbol.

#### Mapping Mastodon to Twitter Accounts.

We utilize Mastodon users’ profiles, noting that they frequently disclose
their additional social media accounts. We first collect user profiles from
each Mastodon server. Then, we extract Twitter handles from URLs in user
profiles. If a Twitter URL is missing, we search the profile’s meta fields for
entries marked with Twitter and extract the handle from this value.

It is worth noting that Mastodon’s decentralized structure poses challenges in
gathering information across all servers (Raman et al. 2019). During our
research, several prominent servers, such as mastodon.social, temporarily
closed new account registrations due to a surge in user traffic. To maintain
the integrity of this study, we concentrated on servers that are highly
available and accessible when using the Mastodon API. Moreover, we utilized
the instances.social API to analyze the number of users for each server.
Specifically, we narrowed down our server selection to those primarily using
English. This allowed us to gather data from the top 10 Mastodon servers that
had the most newly registered users during the research period, as depicted in
Table 1.

### Collecting User Activities and Network Interactions

We utilized APIs, with their authenticated credentials, to monitor and collect
various behaviors of the migrated users included in our study. The collected
data were securely stored in a MongoDB database in an anonymized form,
employing field-level encryption as a security measure.

Our data collection encompassed a variety of activities from detected migrants
such as tweets, retweets, toots, and boosts. Our data collection efforts
included gathering the messages posted during the research period on the
public timelines of both Twitter and Mastodon. This process yielded 1.1M
tweets and 0.5M retweets from Twitter, along with around 0.6M toots and 0.4M
boosts from Mastodon.

We expanded our data collection to encompass followers of detected migrants on
Twitter and Mastodon, resulting in obtaining nearly all available profiles of
the followers of the migrants on these platforms: 85.42% of the total 5.3M
followers on Twitter and 88.72% of the total 2.9M followers on Mastodon. We
also collected data on interactions among the migrants on Mastodon, such as
replies to the postings, and the profiles of users who participated in these
replies.

### Collecting and Grouping User Occupations

Since users may not always include occupations in their profiles, we utilized
Stanford CoreNLP (Manning et al. 2014), a pretrained model capable of
identifying occupation titles through named entity recognition. We first
filtered users by examining Twitter profiles for occupation titles, and if
these are unavailable, we referred to their corresponding Mastodon profiles.
Then, we employed the UK’s Standard Occupational Classification (SOC 2010)
system to assign an occupation per user, widely adopted in prior research on
social media (Sloan et al. 2015). We assigned the user’s occupation title to
the first corresponding three-digit SOC code from the system’s dictionary. If
no match was found, we applied the method suggested by Turrell et al. (2019)
utilizing the user’s profile bio. This method identified the SOC codes for
5,817 of 10,011 users (58.1%). To manage numerous SOC codes, we focused on the
first digit (e.g., 1xx), which denotes the nine major groups. Two annotators
classified these major groups for a random 300-user subset, resulting in a
Cohen’s Kappa of 77.82%. Our approach achieved F1 score of 65.26%, effectively
predicting nine major groups. Figure 4 displays the distribution of
occupational groups among migrated users, with 2xx (Professional Occupations)
and 3xx (Associate Professionals and Technicians) groups being the most
prominent.

´

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.

## RQ1: What Are the Migration Patterns between Twitter and Mastodon?

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). Figure 6: Trends in
attention migrations toward Twitter (blue) and Mastodon (orange). The $x$-axis
represents a date $t_{j}$ when users migrated their attention. The trends are
shown on a daily or weekly basis, with corresponding intervals of $\delta$ = 1
day or $\delta$ = 1 week.

In this section, we investigate the extent to which individuals who were
active on Twitter and Mastodon and how they shifted their attention to each
platform following Musk’s acquisition of Twitter and other major events
related to Twitter.

### Understanding Evolving Migration Patterns

Figure 5 shows the trend between Twitter-related events and the number of
daily active users, covering users active on one or both platforms within
specified intervals. User activity is assessed over a one-day interval,
starting at midnight of the preceding day ($t_{i}$) and ending at midnight of
the current day ($t_{j}$). For example, setting $t_{j}$ to January 2nd means
the interval covers January 1st from 00:00 to 23:59.

To delineate major changes in Figure 5, we examine temporal shifts in daily
active users using Prophet (Taylor and Letham 2018) developed by Facebook, a
time-series method that is capable of handling nonlinear growth and seasonal
changes. Our analysis involved applying Prophet separately to the three trends
named Twitter-only, Mastodon-only, and Twitter & Mastodon. When pinpointing
key moments where these temporal shifts overlapped, we found that these
overlapped points correspond to Mastodon’s first peak (November 22, 2022), and
its second peak (December 20, 2022). Based on these two important moments, we
were able to distinguish three distinct phases in migration patterns: (1) the
protesting phase, (2) the adaptation phase, and (3) the current phase.

The protesting phase began following the change in ownership of Twitter and
concluded before the first peak in usage of Mastodon. During this phase, the
number of active users on Twitter consistently declines, while there is a
steep increase in the number of active users on Mastodon. These changes in the
number of active users appear with various events happened on the Twitter,
such as the mass layoffs at Twitter and the launch of Twitter Blue, which
includes the subscription service for verified accounts and policy updates.

The adaptation phase follows the first peak and continues until the second
peak of Mastodon. In response to recent policy changes on Twitter, there is a
notable change in active user engagement on Mastodon. Especially, the number
of active users on Mastodon temporarily surges after the relaunch of Twitter
Blue and the suspension of Mastodon’s account and several high-profile
journalists on Twitter 111https://www.nytimes.com/2022/12/18/business/twitter-
ban-social-media-competitors-mastodon.html. The similar upward trend is
noticeable for users active on both platforms.

The current phase follows the second peak of Mastodon and continues until the
present time. After Elon Musk announced his intention to step down as CEO of
Twitter when he finds a suitable successor, the number of active users on
Mastodon started to decline. This event may have had a mitigating effect on
attention given to Mastodon. At present, it appears that migrants tend to stay
active on Twitter rather than continuing to use Mastodon, which raises
questions about the longevity of the platform migration.

Figure 6 presents the trends of attention migration on daily and weekly
intervals. These results indicate that the number of users shifting their
attention to Twitter surpasses those moving to Mastodon, especially after each
transition point between the migration phases. Notably, migrated users tend to
shift more to Twitter after experiencing both platforms. The largest gap in
attention migration trends between Twitter and Mastodon emerges during the
adaptation phase. We speculate such a large gap in attention migration is
because numerous users faced challenges in adjusting to Mastodon after
arrival. Overall, the findings reveal that migrated users tend to stay on
Twitter, and suggest that the momentum of migration to Mastodon has weakened.

### Inferring Motivational Factors of Migration

To examine if any events on Twitter prompted users to migrate to Mastodon, we
leveraged BERTopic (Grootendorst 2022) as a tool for examining temporal
variations in their discourses. The frequency of tweets and toots relating to
the top ten topic groups over time are graphically represented in Figure 7.
Our experiment uncovered a significant portion of Twitter-related discourses
(topic group 1 on both Twitter and Mastodon), as well as a strong focus on
Elon Musk (topic group 4 on Twitter and topic group 1 on Mastodon). The
prominent presence of Twitter-related discourses during the protesting phase,
coupled with Elon Musk-related discourses toward the end of adaptation phase,
offer quantitative evidence of a connection between Twitter’s change in
ownership and the subsequent platform migration. Furthermore, our analysis
revealed that 13.6% of the total toots included the keywords “Twitter” or
“Elon Musk,” while only 6.1% of the total tweets contained these keywords.
This difference shows that migrated users were proportionally more inclined to
discuss Twitter and Elon Musk on Mastodon than on Twitter.

Identifying trending discourses, however, does not provide insight into the
motivations for the migration. To fill this gap, we utilized sentiment
analysis, leveraging a DeBERTa-based model (He et al. 2020; Yang et al. 2021),
to investigate if Musk’s acquisition led users to leave Twitter as a form of
protest. This model, adeptly fine-tuned for aspect-based sentiment analysis,
allowed us to gauge sentiments specifically targeted at either Twitter or Elon
Musk, in addition to capturing the general sentiment from a non-specific
target.

Figure 8 illustrates the distribution of sentiment scores of tweets and toots
on each platform. Our observations suggest that Mastodon may be particularly
appealing to individuals with negative sentiments toward Twitter and its
leadership. The migration from Twitter to Mastodon aligns with the push-pull
theory of migration (Lee 1966), wherein the relatively negative sentiment
toward Twitter and its leadership may act as a “push factor” (Newell et al.
2016) for users to leave Twitter. In contrast, users show more positive
sentiment when Mastodon is a target than Twitter, which can serve as a “pull
factor” attracting users to Mastodon (Hou and Shiau 2020).

Figure 7: The trends in the number of tweets on Twitter and toots on Mastodon
that mention one of the top 10 topic groups. Each topic group is shown with a
discourse consisting of its four most frequently occurring words, separated by
underscores. The topic groups are numbered based on their rank of proportion
to the total messages, where a lower rank signifies a higher proportion. Note
that the $x$-axis represents the exact date when the tweet or toot was
published. As a result, the dates for the protesting, adaptation, and current
phases are a day earlier than those referenced in Figure 5. Figure 8: The
sentiment score distribution is compared between Twitter (blue) and Mastodon
(orange). We compare the distributions of Twitter and Mastodon across three
categories: (1) sentiment without a target; (2) sentiment with Twitter as a
target ; and (3) sentiment with Elon Musk as a target. The scores range from
-1 to 1, with lower values indicating a more negative sentiment. The mean
score for each distribution is shown by the dashed lines. The Kolmogorov-
Smirnov test revealed significant sentiment differences between Twitter and
Mastodon in all three categories, with a significant $p$-value of less than
0.001.

## RQ2: Distinct User Behaviors of Migrated Users on Twitter and Mastodon

Addressing this research question allows us to compare how migrated users
exhibit their behaviors distinctively on Twitter and Mastodon, and discern if
their varying behaviors can be explained by the unique platform architectures.
Moreover, we also aim to uncover the disparities between these platforms based
on the responses that users attracted on each platform.

### User-level Features

To explore how users exhibit different behaviors on each platform, we decided
to compare the common user behaviors that are shared between Mastodon and
Twitter, such as tweets and toots. Given a specific user $u$ and a platform
$p$, we define three distinct types of user-level features on each platform:
(1) User Activity, (2) User Network, and (3) User Response. The definitions
for these categories are as follows:

####  User Activity.

$\mathcal{A}(u,p)$ is determined by a user’s two activity types denoted as
$\\{a_{i}\\}^{2}_{i=1}$, which compromise status messages and reshares (e.g.,
tweets and retweets a user has posted). The counting function
$\sigma(u,a_{i},p)$ measures the cumulative count of the activity $a_{i}$
during the studied period. Formally, the user activity can be represented as:

$\mathcal{A}(u,p)=\sum\limits_{i=1}^{2}\frac{\sigma(u,a_{i},p)-\min_{u^{\prime}}\left(\sigma(u^{\prime},a_{i},p)\right)}{\max_{u^{\prime}}\left(\sigma(u^{\prime},a_{i},p)\right)-\min_{u^{\prime}}\left(\sigma(u^{\prime},a_{i},p)\right)}$
(1)

####  User Network.

$\mathcal{W}(u,p)$ is derived by two network types denoted as
$\\{w_{i}\\}^{2}_{i=1}$, which include followers and followees of a user. The
counting function $\sigma(u,w_{i},p)$ measures the size of the network
$w_{i}$. The user network can be defined as:

$\mathcal{W}(u,p)=\sum\limits_{i=1}^{2}\frac{\sigma(u,w_{i},p)-\min_{u^{\prime}}\left(\sigma(u^{\prime},w_{i},p)\right)}{\max_{u^{\prime}}\left(\sigma(u^{\prime},w_{i},p)\right)-\min_{u^{\prime}}\left(\sigma(u^{\prime},w_{i},p)\right)}$
(2)

####  User Response.

$\mathcal{R}(u,p)$ is obtained by two response types denoted as
$\\{r_{i}\\}^{2}_{i=1}$, which include favorites and reshare a user has
received (e.g., likes and retweets a user received). The counting function
$\sigma(u,r_{i},p)$ measures the cumulative count of the response $r_{i}$
during the studied period. Formally, the user response can be represented as:

$\mathcal{R}(u,p)=\sum\limits_{i=1}^{2}\frac{\sigma(u,r_{i},p)-\min_{u^{\prime}}\left(\sigma(u^{\prime},r_{i},p)\right)}{\max_{u^{\prime}}\left(\sigma(u^{\prime},r_{i},p)\right)-\min_{u^{\prime}}\left(\sigma(u^{\prime},r_{i},p)\right)}$
(3)

where $u^{\prime}$ denotes a user within the population under study. Please
note that we utilize max-min normalization on each platform when calculating
$\mathcal{A}(u,p)$, $\mathcal{W}(u,p)$, and $\mathcal{R}(u,p)$. This
normalization accounts for the different scales of user behaviors present on
each platform.

### Disparity in User-level Features

We conducted a comparative analysis of three distinct user-level features on
both Twitter and Mastodon platforms. Our comprehensive study derives insights
from Table 2, which shows the mean and mean absolute deviation values, along
with Figure 9, that illustrates the interquartile plot. Based on this
analysis, we make three primary observations as follows:

Figure 9: Interquartile box plots comparing the values of user-level features
in Twitter (blue) and Mastodon (orange). Within each box plot, the red line
indicates the median value.

First, we observe that users exhibit, on average, $\sim$4 times higher user
activity on Twitter than on Mastodon. This disparity may stem from the fact
that numerous users continued to use both platforms even after migration, with
Twitter serving as their primary platform (He et al. 2023). Additionally, the
higher MAD value on Twitter indicates a broader spread of activities compared
to Mastodon.

Second, we find that users show, on average, $\sim$1.4 times more user network
on Mastodon than on Twitter. This indicates that users tend to focus on
building connections with other users on Mastodon (Shaw 2020) than Twitter.
The MAD value is higher on Mastodon, signifying a more varied distribution of
network sizes among the migrated users.

Third, our analysis reveal that users, on average, receive $\sim$1.3 times
more user response on Twitter than on Mastodon, likely due to Mastodon’s lower
Q1 value (0.00003) compared to Twitter’s (0.003). The higher MAD value on
Twitter suggests that user responses are more well spread among users on
Twitter. Notably, a larger percentage of users receive zero responses on
Mastodon (13.6%) than on Twitter (3.2%), consistent with previous findings of
overall lower response rates on Mastodon (La Cava, Greco, and Tagarelli
2022b).

Metric | $\mathcal{A}$ | $\mathcal{W}$ | $\mathcal{R}$
---|---|---|---
A | B | A | B | A | B
AVG | 0.020 | 0.005 | 0.007 | 0.010 | 0.005 | 0.004
MAD | 0.023 | 0.006 | 0.006 | 0.010 | 0.008 | 0.006
Table 2: Statistics of user-level features in platform A (Twitter) and B
(Mastodon). AVG is average, and MAD (Mean Absolute Deviation) shows the
dispersion from the average.

### Disparity in Occupations to User Response

Figure 10: Gini coefficients for Twitter (blue) and Mastodon (orange) depict
occupational distribution among migrants, focusing on users above the top 50%
response rate. The red dashed line is the Gini coefficient for the original
sample.

We utilized the Gini coefficient, a widely accepted metric for assessing
disparities in income or wealth among diverse groups (Gini 1936), to evaluate
the inequality in occupational distribution. The Gini coefficient values range
between 0 and 1, with lower values signifying greater equality. We computed
the Gini coefficient using $n$ occupation groups (i.e., the nine major groups
in the UK’s SOC code) and $x_{i}$, representing the user count for the $i$-th
group among the 5,817 users (58.1% of the 10,011 users) with identifiable
occupations as follows:

$Gini\>Coefficient=1-\sum_{i=1}^{n}\left(\frac{x_{i}}{\sum_{j=1}^{n}x_{j}}\right)^{2}$
(4)

The Gini coefficient for 5,817 users is calculated to be 0.627. Such high
inequality mainly stems from the overrepresentation of user groups like SOC
2xx (Professionals) and SOC 3xx (Associate Professionals and Technicians). One
potential reason for this inequality is the unique appeal that Mastodon’s
decentralized platform holds for individuals with academic or technology-
related backgrounds (Kupferschmidt 2022; Shaw 2020; Zignani, Gaito, and Rossi
2018).

To discern if certain groups of occupations are encouraged to attract more
user responses on each platform, we examined the occupational inequality of a
subset in each platform. Our primary focus was on the relationship between
occupational inequality and the user response on each platform. To this end,
we first ranked users according to their responses on each platform. Once we
created the ranking, we focused on the users who actively engage on each
platform and these users represent a value above the median in the data.

Figure 10 demonstrates that occupational inequality on Twitter surpasses the
inequality of the original sample within the top 50% of users. Interestingly,
the disparity between Twitter and Mastodon peaks within the highest 2.5% of
users, underlining a significant inequality among this extremely popular user
group on Twitter. Conversely, on Mastodon, users exhibit lower inequality than
the original sample up to the top 50% of users, suggesting a more evenly
distributed occupational distribution among its highly engaged users.

### Disparity in Hashtags to User Response

We compared the unique set of hashtags shared by each user with their
popularity on Twitter and Mastodon. Given that a user’s popularity on one
platform, gauged by the size of their user response, may not necessarily
translate to popularity on another platform, we grouped users into four
categories based on their popularity: (1) popular on Twitter, but not
Mastodon; (2) popular on Mastodon, but not Twitter; (3) popular on both
platforms; and (4) unpopular on both platforms.

Users were grouped into one of four categories based on their ranks on each
platform, $rank_{A}$ for Twitter and $rank_{B}$ for Mastodon. For categories
(1) and (2), users are sorted in descending order based on the difference in
their ranks ($rank_{diff}=rank_{A}-rank_{B}$). This represents users with the
largest differences in user response between the two platforms, with the top
10% being users who are popular on Mastodon but not on Twitter and the bottom
10% being users who are popular on Twitter but not on Mastodon. For categories
(3) and (4), users are sorted based on the sum of their ranks
($rank_{sum}=rank_{A}+rank_{B}$). The top 10% in this case are users who are
unpopular on both platforms, while the bottom 10% were users who are popular
on both platforms.

Figure 11: Wordclouds of unique lowercase hashtags on two platforms A
(Twitter) and B (Mastodon). Hashtags are categorized based on the popularity
of migrants: (1) popular on Twitter but not on Mastodon, (2) popular on
Mastodon but not on Twitter, (3) popular on both platforms, and (4) unpopular
on both platforms. More frequent hashtags are highlighted with larger font
size.

Figure 11 showcases frequently used unique hashtags, classified into four
categories based on the popularity of users on each platform. On Twitter, we
observed popular users predominantly focus on social issue hashtags, such as
#Covid and #Ukraine, whereas their less popular counterparts are inclined
toward technology-oriented discussions, evident by those frequent hashtags
such as #Opensource, #Security, and #Python. This suggests sharing global and
social hashtags tend to be the main focus for getting more response on
Twitter. In contrast, Mastodon users of all popularity levels engage with
niche contents, encompassing photography, gaming, cats, and programming.
Notably, users often share migration-specific hashtags on Mastodon, such as
#TwitterMigration and #Introduction, which are relatively uncommon among the
users popular on Twitter.

### Analysis Summary

Our findings suggest empirical evidence indicating that the unique
architectures of these platforms influence two aspects:

  * •

The occupational inequality among popular users is more pronounced on Twitter
than on Mastodon. This can be due to Twitter’s design, which focuses on
popularity by promoting well-known users and their posts (La Cava, Greco, and
Tagarelli 2021). In contrast, Mastodon’s topic-oriented approach creates
connections and interactions based on shared interests. This approach provides
an opportunity for migrated users from diverse occupations to gain popularity.

  * •

Popular users on Twitter often share hashtags centered on global and societal
issues. We speculate that this tendency could be linked to Twitter’s central
structure, which enables broader reach and a larger audience, thereby
amplifying the popularity of users with social significance (Kwak et al. 2010;
Lehmann et al. 2012). Conversely, Mastodon users create connections by seeking
out those with similar interests, promoting the sharing of more niche content,
relatively irrespective of the users’ popularity levels.

Future research aims to enhance our understanding of the communication
dynamics between Twitter and Mastodon. To investigate how information
consumption is influenced among migrating users, further studies should focus
on: (1) the impact of disparities between Twitter and Mastodon on the
communication structure of both platforms; (2) the effects of disparities in
occupational distribution on the overall user experience and information
sharing; and (3) how these imbalances can inform strategies to improve user
experience and promote equitable engagement among diverse users.

## RQ3: Sustainability of Platform Migration:
A Case Study of Mastodon

### Behavioral Characteristics of Residents

In our investigation of user residency on Mastodon, we categorized the
migrated users into two distinct groups: (1) Non-residents, whose last
activity on Mastodon was either during the protesting or adaptation phase, and
(2) Residents, whose last activity on Mastodon is during the current phase.

In analyzing the behavioral aspects that retain users on Mastodon, we applied
user-level features to predict the two types of migrants. This investigation
involved unique user-level features for Mastodon such as (1) Interaction
Diversity, (2) Fandom Migration, and (3) Migration Hashtags.

#### Interaction Diversity.

We conducted a study on interaction diversity for a specific user, denoted as
$u$. This metric measures the level of interaction for a user $u$ to engage
with other users from different primary servers. The Shannon entropy formula
is utilized to compute this metric as follows:

$\displaystyle H(u)=-\sum_{i=1}^{|\mathcal{S}|}P(u,s_{i})\log P(u,s_{i}),$ (5)
$\displaystyle
P(u,s_{i})=\frac{\sigma(u,s_{i})}{\sum_{j=1}^{|\mathcal{S}|}\sigma(u,s_{i})},$

where $\sigma(u,s_{i})$ is the cumulative count of interactions with users
from server $s_{i}\in\mathcal{S}$, with interactions defined as receiving
follows or replies by other users. $P(u,s_{i})$ is the probability of user $u$
interacting with users from server $s_{i}$.

#### Fandom Migration.

We examined the concept of “Fandom migration” (Fiesler and Dym 2020), a
phenomenon in which a leader’s migration to a new platform often leads to a
collective migration of their followers from the previous platform as well. To
determine the percentage of a user’s Mastodon followers who had previously
followed them on Twitter, we analyzed identical usernames on both platforms,
under the premise that identical usernames belong to the same person.

#### Migration Hashtags.

To understand if users who led the migration from Twitter to Mastodon were
more likely to remain, we analyzed the frequency of migration-specific
hashtags, including: #RipTwitter, #GoodbyeTwitter, #JoinMastodon,
#MastodonMigration, #MastodonSocial, #ByebyeTwitter, #TwitterMigration,
#TwitterTakeover, #TwitterShutdown, #LeaveTwitter, #TwitterRefugee and
#TwitterExodus.

For our analysis to classify the two types of migrants, namely non-residents
and residents, we performed statistical tests on six distinct user-level
characteristics. We found that a total of 5,590 residents (55.83% of the
10,011 users) were present at the time of the study. The outcomes of the
logistic regression, after standardization of the feature values, are
presented in Table 3. These results demonstrate the importance of various
user-level features in predicting the types of migrants. Among the examined
features, user activity, user network size, diversity of interactions, and
fandom migration emerge as highly significant features, each demonstrating
p-values lower than 0.001. Positive coefficients of these features reveal a
positive association with the types of migrants, suggesting that as the value
of these feature increases, so does the likelihood of migrated users being
residents. Remarkably, user activity and interaction diversity show
distinctively higher coefficients than other features, indicating their strong
association with user retention to Mastodon.

Surprisingly, the volume of user response is not significantly associated with
a user’s decision to stay on Mastodon, implying that users who merely focus on
attracting a large user response tend to discontinue using the platform over
time. Moreover, there is no statistical correlation between the frequency of
sharing migration-specific hashtags and migrant types, indicating that
individuals who once actively led the migration movement may not necessarily
stay on Mastodon.

Features | Coef. | SE |  $p$-value | OR
---|---|---|---|---
User Activity | 0.914 | 0.081 | 0.000** | 2.494
User Network | 0.173 | 0.040 | 0.000** | 1.189
User Response | -0.053 | 0.032 | 0.100 | 0.948
Interaction Diversity | 0.724 | 0.027 | 0.000** | 1.077
Fandom Migration | 0.074 | 0.022 | 0.000** | 1.076
Migration Hashtags | -0.041 | 0.028 | 0.147 | 0.959
Table 3: Results on logistic regression to predict the two types of migrants
(non-resident and resident) on Mastodon. Coef represents coefficient, SE
stands for standard error, and OR denotes odd ratio. Note that *$p<0.05$ and
**$p<0.001$.

### Analysis Summary

Mastodon, a decentralized social media platform, uses a federated network to
encourage connections based on shared interests. However, this can lead to
social fragmentation, complicating user interaction during building social
networks. (Raman et al. 2019). Our analysis yields two notable outcomes:

  * •

The decision to remain on Mastodon primarily is associated with a user’s
active effort to frequent conversations and diverse social interactions to
appreciate the community-centered experience, rather than by the volume of
responses received or the frequent sharing of migration hashtags.

  * •

Mastodon distinguishes itself by prioritizing community-centered experiences,
contrasting with traditional platforms that often give preference to
individual self-promotion or the creation of viral content to capture a
broader audience.

As a prospective avenue to enhance the sustainability of platform migration,
it may be helpful to examine social network analysis theories that can shed
light on concealed patterns within the social networks of migrated users by
pinpointing information brokers linking disconnected groups, or structural
holes (Burt 2018) and by examining the impact of weak ties in loosely
connected networks on obtaining unique information and resources (Granovetter
1973). In turn, this information can inform platform developers, community
managers, and users about the factors that facilitate sustainability of
migration to a platform with distinct architecture and reduce mass departures
and provide insight on how to avoid similar issues when launching new social
media.

## Limitations

First, the data available for this study do not include the 266 users who
experienced permanent migration by January 2, 2023, due to API restrictions
preventing access to deleted accounts on both Twitter and Mastodon. Our data
is also limited to the first ten weeks post Elon Musk’s Twitter acquisition.
As such, the findings from the migration patterns might not be applicable to
other periods or predictive of future trends.

Second, we acknowledge the presence of alternative platforms for Twitter, such
as Hive (a microblogging service) and Damus (decentralized network powered by
Nostr protocol). However, our study focuses on the migration from Twitter to
Mastodon, assuming that user attention mainly oscillates between Twitter and
Mastodon due to Mastodon’s increased popularity during the time that Musk
acquired Twitter.

Last, our study does not provide a causal analysis. Instead, the motivational
factors are inferred from observed data on migration patterns, discourses over
time, and users’ sentiment towards them. Testing the correctness of the
inferred motivations for migration requires surveys of users including open-
ended interviews, where users can report their reasons for migrating or not
from any platform.

## Conclusion

We introduce two types of migration on social media and explore the migration
patterns between Twitter and Mastodon. Our observations indicate that
dissatisfaction with Twitter and its management, notably the Twitter’s
ownership change, is a primary motivation for users to migrate to Mastodon.
Interestingly, these migrated users often utilize both platforms, frequently
shifting their attention back to Twitter after trying both platforms. This
migration pattern refutes claims of dooms day scenario for Twitter after the
mass exodus.

Our comparison of user behaviors on Twitter and Mastodon unveils notable
disparities between the two platforms. Specifically, users exhibit a larger
inequality in the distribution of users’ occupations within highly engaged
users on Twitter. An analysis of hashtag usage reveals that popular Twitter
users typically concentrate on global and social issues, while Mastodon users,
regardless of their popularity, often share hashtags related to migration and
niche content.

We identify key user behaviors that are associated with the retention of users
on Mastodon, with the variety of interactions with users across different
Mastodon servers being a highly significant factor. Conversely, the volume of
user responses, such as favorites and reshares, does not exhibit a significant
impact on user retention. This underscores the distinctive appeal of
Mastodon’s features, such as its decentralized architecture and community-
oriented interactions, and how they relate to user behaviors.

In conclusion, our findings offer valuable insights for platform migration and
how to retain users during periods after a mass migration. By leveraging these
behavioral factors, platform designers can create more engaging and
sustainable platforms that cater to user preferences and needs.

## Future Work

Exploring the phenomenon of returning migration is an important area for
future research. Our plan is to study this phenomenon as part of a long-term
analysis when more user migration data becomes available between Twitter and
Mastodon (Jeong et al. 2024). Furthermore, we aim to understand how the
disparities between Twitter and Mastodon impact each platform, including user
experience and the way information is shared among migrated users, including
the spread of disinformation (Jeong et al. 2022a, b). One aspect of this
analysis is to apply theories of weak ties (Granovetter 1973) and structural
holes (Burt 2018). Furthermore, surveys will be conducted to complement the
findings and provide qualitative evidence of the potential motivations behind
migration. Finally, the proposed approach will be extended to other
alternative social media platforms beyond Twitter, to gather more
comprehensive understanding of the migration.

## Appendix

### Standard Occupational Classification (SOC)

The UK’s SOC 2010 system uses a hierarchical structure to organize
occupations, where the first digit of each SOC code represents the nine major
groups, as shown in Table 4

Major Group |  Description
---|---
1xx |  Planing, directing, or coordinating the operations of businesses or other organizations.
2xx |  Specialized tasks that require advanced knowledge, such as law, science, or education.
3xx |  Support in various professional fields, such as healthcare, science, or engineering.
4xx |  Administrative and clerical duties for business, government agencies or organizations.
5xx |  Work in skilled jobs, such as construction, mechanics, or electrical work.
6xx |  Personal services to individuals or groups, such as childcare, leisure, or personal care.
7xx |  Selling products or services to customers or provide customer support.
8xx |  Operating machinery or perform manual labor in manufacturing or production.
9xx |  Simple and routine tasks that do not usually require formal education or training.
Table 4: Nine major groups in the UK’s SOC 2010 system.

### Experimental Details

Utilizing the Prophet model set to change point prior scale of 0.252 and
change point range of 0.95 with linear growth, we observed eight temporal
shifts in Twitter-only users on specific dates (Nov. 1, 9, 22, 24, Dec. 5, 10,
20, 28), five in Mastodon-only users (Nov. 11, 19, 22, Dec. 7, 20), and five
in Mastodon & Twitter users (Nov. 11, 19, 22, Dec. 5, 20). In BERTopic model,
we utilized all-MiniLM-L6-v2 as the default model, which is a pre-trained
transformer model designed for embedding English sentences and short
paragraphs. Focusing on toots and tweets in English, we optimized our system
settings to auto-adjust with a minimum of 100 topics, which resulted in
generating 841 topics for tweets and 483 for toots. For aspect-based sentiment
analysis, we leveraged the DeBERTa with deberta-v3-large-absa, which is a
fine-tuned model for ABSA (Aspect-Based Sentiment Analysis) datasets. The
model’s default parameter setup has a hidden size of 768, 12 hidden layers,
and 12 attention heads.

## Ethical Statement, Impact, and Reproducibility

Our proposed approach for analyzing migration from Twitter to Mastodon may
suffer from selection bias, as it relies on users who have chosen to disclose
their other social media platform accounts on their profiles. As a result, the
selected users may not be representative of the larger population of users
migrating from Twitter to Mastodon.

Our study aims to gain insights into the migration of users between Twitter
and Mastodon, and is not intended to exploit or manipulate these communities
for financial gain. Our data collection was completed before Twitter updated
its basic API tier to a priced model. In compliance with the terms of service
of Twitter and Mastodon, we disclose only the user IDs of the accounts
included in our study. The codes used for mapping user accounts between
Twitter and Mastodon are available at our
repository222https://github.com/ujeong1/ICWSM24-Exploring-Platform-Migration-
Patterns-between-Twitter-and-Mastodon

## Acknowledgments

This work received support from the Office of Naval Research (Award No.
N00014-21-1-4002) and the National Science Foundation (Award No. 2227488).
Opinions, interpretations, conclusions, and recommendations within this
article are solely those of the authors. Special acknowledgment goes to Dr.
Kaize Ding, Assistant Professor at Northwestern University, whose insightful
feedback was instrumental in improving the quality of the article.

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for the reseasrch.

    2. (b)

Did you mention the license of the assets? No, but we confirmed BERTopic,
DeBERTa, StanfordCoreNLP, and Prophet are available under the MIT License. The
occupation encoder used in this paper utilizes the UK’s SOC system and the
code is under the GNU General Public License.

    3. (c)

Did you include any new assets in the supplemental material or as a URL? Yes,
see the Ethical Statement, Impact, and Reproducibility.

    4. (d)

Did you discuss whether and how consent was obtained from people whose data
you’re using/curating? NA

    5. (e)

Did you discuss whether the data you are using/curating contains personally
identifiable information or offensive content? Yes, see the Ethical Statement,
Impact, and Reproducibility.

    6. (f)

If you are curating or releasing new datasets, did you discuss how you intend
to make your datasets FAIR? No, because Twitter has changed its API to a
pricing model, it limits accessibility to the protocol at the moment.

    7. (g)

If you are curating or releasing new datasets, did you create a Datasheet for
the Dataset? No, it is not available at this time due to the terms and
conditions on Twitter (or X) for developers. See the Ethical Statement,
Impact, and Reproducibility sections for more details.

  6. 6.

Additionally, if you used crowdsourcing or conducted research with human
subjects, without compromising anonymity…

    1. (a)

Did you include the full text of instructions given to participants and
screenshots? NA

    2. (b)

Did you describe any potential participant risks, with mentions of
Institutional Review Board (IRB) approvals? NA

    3. (c)

Did you include the estimated hourly wage paid to participants and the total
amount spent on participant compensation? NA

    4. (d)

Did you discuss how data is stored, shared, and deidentified? NA