361 lines
19 KiB
Plaintext
361 lines
19 KiB
Plaintext
---
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title: Recommending Servers on Mastodon
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short-title: Mastodon Recommendations
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authors:
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- name: Carl Colglazier
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affiliation:
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name: Northwestern University
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city: Evanston
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state: Illinois
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country: United States
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corresponding: true
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bibliography: references.bib
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pdf-engine: pdflatex
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format:
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html: default
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pdf+icwsm:
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fig-pos: 'ht!bp'
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cite-method: natbib
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template: template.tex
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keep-md: true
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link-citations: false
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acm-pdf:
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output-file: mastodon-recommendations-acm.pdf
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acm-metadata:
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# comment this out to make submission anonymous
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anonymous: true
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# comment this out to build a draft version
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#final: true
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# comment this out to specify detailed document options
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# acmart-options: sigconf, review
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# acm preamble information
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copyright-year: 2018
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acm-year: 2018
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copyright: acmcopyright
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doi: XXXXXXX.XXXXXXX
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conference-acronym: "Conference acronym 'XX"
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conference-name: |
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Make sure to enter the correct
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conference title from your rights confirmation emai
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conference-date: June 03--05, 2018
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conference-location: Woodstock, NY
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price: "15.00"
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isbn: 978-1-4503-XXXX-X/18/06
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# if present, replaces the list of authors in the page header.
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shortauthors: Colglazier
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# The code below is generated by the tool at http://dl.acm.org/ccs.cfm.
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# Please copy and paste the code instead of the example below.
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ccs: |
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\begin{CCSXML}
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<ccs2012>
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<concept>
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<concept_id>10010520.10010553.10010562</concept_id>
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<concept_desc>Computer systems organization~Embedded systems</concept_desc>
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<concept_significance>500</concept_significance>
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</concept>
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<concept>
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<concept_id>10010520.10010575.10010755</concept_id>
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<concept_desc>Computer systems organization~Redundancy</concept_desc>
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<concept_significance>300</concept_significance>
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</concept>
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<concept>
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<concept_id>10010520.10010553.10010554</concept_id>
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<concept_desc>Computer systems organization~Robotics</concept_desc>
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<concept_id>10003033.10003083.10003095</concept_id>
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<concept_desc>Networks~Network reliability</concept_desc>
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<concept_significance>100</concept_significance>
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</concept>
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</ccs2012>
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\end{CCSXML}
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\ccsdesc[500]{Computer systems organization~Embedded systems}
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\ccsdesc[300]{Computer systems organization~Redundancy}
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\ccsdesc{Computer systems organization~Robotics}
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\ccsdesc[100]{Networks~Network reliability}
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keywords:
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- decentralized online social networks
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abstract: |
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When trying to join the Fediverse, a decentralized collection of interoperable social networking websites, new users face the dillema of choosing a home server. Using trace data from millions of new Fediverse accounts, we show that new accounts on the flagship server are less likely to remain active and that accounts that move between servers tend to move from larger servers to smaller server. We then use the insights from our analysis to build a tool that can help new Fediverse users find servers with a high probability of being a good match based on their interests. Based on simulations, we demonstrate that such a tool can be effective even with limited data on each local server.
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execute:
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echo: false
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error: false
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warning: false
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message: false
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freeze: auto
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fig-width: 6.75
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knitr:
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opts_knit:
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verbose: true
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#filters:
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# - parse-latex
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---
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# Introduction
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The Fediverse has emerged as a viable alternative to corporate, centralized social media such as Twitter and Reddit. Over the course of the last two years, millions of people have set up new accounts, significantly increasing the size of the network. In the wake of Elon Musk's Twitter aquisition, Mastodon, a popular Fediverse software which offers a Twitter-like experience, saw in increase in activity and scrutiny.
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We show how the onboarding process for Mastodon has changed over time with a particular focus on the largest, flagship Mastodon server. Users who sign up to this server are less likely to remain active. Based on data from over a million Mastodon accounts, we also find that many users who move accounts tend to gravitate toward smaller, more niche servers over time.
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We design a potential way to create server and tag recommendations on Mastodon, which could both help newcomers find servers that match their interests and help established accounts discover "neighborhoods" of related servers.
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# Background
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## Empirical Setting
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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.
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Discovery has been challenging on Masotodon. The developers and user base tend to be skeptical of algorithmic intrusions, instead opting for timelines which only show posts in reverse chronological order. Search is also difficult. Public hashtags are searchable, but most servers have traditionally not supported searching keywords or simple strings. Accounts can only be searched using their full `username@server` form.
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Mastodon features a "home" timeline which shows all public posts from accounts that share the same home server. 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.
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Mastodon offers its users high levels of data portability. Users can move their accounts accross instances while retaining their follows (their post data; however, does not move with the new account). The choice of an initial instance consequentially is not irreversible.
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## Newcomers in Online Communities
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Onboarding newcomers is an important part of the lifecycle 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 [@krautBuildingSuccessfulOnline2011 p. 182]. However, the process of onboarding newcomers is not always straightforward. Newcomers may have difficulty finding the community, understanding the norms and expectations, and finding a place for themselves within the community. This can lead to high rates of attrition among newcomers.
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## The Mastodon Migrations
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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 [@heFlockingMastodonTracking2023] and driving interest in Mastodon in a process @cavaDriversSocialInfluence2023 found consistent with social influence theory.
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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 [@driscollWeMisrememberEternal2023]. Many Mastodon servers do have specific norms which people coming from Twitter may find confusing, such as local norms around content warnings [@nicholsonMastodonRulesCharacterizing2023]. Variation amoung 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 [@diazUsingMastodonWay2022].
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Some media outlets have framed reports on Mastodon [@hooverMastodonBumpNow2023] through what @zulliRethinkingSocialSocial2020 calls the "Killer Hype Cycle", whereby the media finds a new alterntive social media platform, declares it a potential killer of some established platform, and laters 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.
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# Data
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```{r}
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#| label: fig-account-timeline
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#| fig-cap: "Accounts in the dataset created between January 2022 and March 2023. The top panels shows the proportion of accounts still active 45 days after creation, the proportion of accounts that have moved, and the proportion of accounts that have been suspended. The bottom panel shows the count of accounts created each week. The dashed vertical lines in the bottom panel represent the annoucement day of the Elon Musk Twitter acquisition, the acquisition closing day, a day where Twitter suspended a number of prominent journalist, and a day when Twitter experienced an outage and started rate limiting accounts."
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#| fig-height: 3
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#| fig-width: 6.75
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#| fig-env: figure*
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#| fig-pos: htb!
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library(here)
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source(here("code/helpers.R"))
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account_timeline_plot()
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```
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**Mastodon Profiles**: We collected accounts using data previously collected from posts on public Mastodon timelines from October 2020 to January 2024. We then queried for up-to-date information on those accounts including their most recent status and if the account had moved. This gave us a total of N accounts. Note that because we got updated information on each account, we include only accounts on servers which still exist and which returned records for the account.
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**Moved Profiles**: We found a subset of N accounts which had moved from one server to another.
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**Tags**: We collect N posts which contained between 2 and 5 hashtags.
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# Analysis and Results
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## Competition Among Servers in Attracting Newcomers
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_How does mastodon.social factor into the aggregate Mastodon onboarding process?_
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::: {#fig-mastodon-online-signup-disabled width=50% .content-visible when-format="html"}
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{fig-env="figure" width=6cm height=6cm}
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The main page of mastodon.social as viewed by a logged out web browser on November 1, 2020. The sign-up form is blurred out and instead there is a message suggesting to either sign up on mastodon.online or see a list of servers accepting new accounts at joinmastodon.org.
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:::
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Throughout its history, Mastodon's flagship server, mastodon.social, has allowed and disallowed open sign-ups at various times. When the website did not allow sign-ups, it displayed a message redirecting those interested in signing up for an account to mastodon.social or alternatively to a list of potential servers at joinmastodon.com.
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We found three main periods during which mastodon.social did not accept new signups by first noting the times in @fig-account-timeline where the proportion of new accounts on mastodon.social drops to zero. We then used the Internet Archive to verify that signups were disabled during these periods.
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1. An extended period of through the end of October 2020.
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2. A temporary issue when the email host limited the server in mid-2022.
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3. Two periods in late 2022 and early 2023.
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We construct an interrupted time series using an autoregressive integrated moving average (ARIMA) model for sign-ups on mastodon.social, the servers linked in joinmastodon.org, and all other servers. For the first period, we also include mastodon.online since mastodon.social linked to it directly during that time.
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::: {.content-visible when-format="html"}
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$$
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\begin{aligned}
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y_t &= \beta_0 + \beta_1 \text{open}_t + \beta_2 \text{day}_t + \beta_3 (\text{open} \times \text{day})_t \\
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&\quad + \beta_4 \sin\left(\frac{2\pi t}{7}\right) + \beta_5 \cos\left(\frac{2\pi t}{7}\right) \\
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&\quad + \beta_6 \sin\left(\frac{4\pi t}{7}\right) + \beta_7 \cos\left(\frac{4\pi t}{7}\right) \\
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&\quad + \phi_1 y_{t-1} + \phi_2 y_{t-2} + \epsilon_t
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\end{aligned}
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$$
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where $y_t$ is the number of new accounts on a server at time $t$, $\text{open}_t$ is a binary variable indicating if the server is open to new sign-ups, $\text{day}_t$ is an increasing integer represnting the date, and $\epsilon_t$ is a white noise error term. We use the sine and cosine terms to account for weekly seasonality.
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| Period | Setting | Significant |
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|------------|:----------------|:----|
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| 2020-2021 | mastodon.online | Yes |
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| | JoinMastodon | No |
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| | Other | No |
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| Mid 2022 | JoinMastodon | No |
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| | Other | No |
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| Early 2022 | JoinMastodon | No |
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| | Other | No |
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: Results from ARIMA models for the number of new accounts on mastodon.social, mastodon.online, servers linked in joinmastodon.org, and all other servers.
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:::
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::: {.content-visible when-format="pdf+icwsm}
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```{=latex}
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\begin{table}[!ht]
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\centering
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\begin{tabular}{|l|l|l|}
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\hline
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Period & Setting & Significant \\ \hline
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2020-2021 & mastodon.online & Yes \\ \hline
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~ & JoinMastodon & No \\ \hline
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~ & Other & No \\ \hline
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Mid 2022 & JoinMastodon & No \\ \hline
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~ & Other & No \\ \hline
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Early 2022 & JoinMastodon & No \\ \hline
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~ & Other & No \\ \hline
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\end{tabular}
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\end{table}
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```
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:::
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## Survival Model
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_Are accounts on mastodon.social less likely to remain active than accounts on other servers?_
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```{r, cache.extra = tools::md5sum("code/survival.R")}
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#| cache: true
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#| label: fig-survival
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#| fig-env: figure
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#| fig-cap: "Survival probabilities for accounts created during May 2023."
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#| fig-width: 3.375
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#| fig-height: 2.5
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#| fig-pos: h!
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library(here)
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source(here("code/survival.R"))
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plot_survival
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```
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Using accounts created during May 2023, we create Kaplan–Meier estimator for the probability that an account will remain active based on whether the account is on mastodon.social or otherwise if it is on a server in the Join Mastodon list. An account is considered active if it posted a status on or after December 1, 2023 and all accounts which posted after that point are considered censored.
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The results suggest that accounts on mastodon.social are less likely to remain active than accounts on other servers, but there is no significant difference between accounts on servers in the Join Mastodon list and other servers.
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## Moved Accounts
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_Do accounts tend to move to larger or smaller servers?_
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Mastodon users can move their accounts to another server while retaining their connections (but not their posts) to other Mastodon accounts. This feature, built into the Mastodon software, offers data portability and helps avoid lock-in.
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```{r}
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#| label: ergm-table
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#| echo: false
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#| warning: false
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#| message: false
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#| error: false
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library(here)
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library(modelsummary)
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library(kableExtra)
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library(purrr)
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library(stringr)
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load(file = here("data/scratch/ergm-model-early.rda"))
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load(file = here("data/scratch/ergm-model-late.rda"))
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if (knitr::is_latex_output()) {
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format <- "latex"
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} else {
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format <- "html"
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}
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x <- modelsummary(
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list("Coef." = model.early, "Std.Error" = model.early, "Coef." = model.late, "Std.Error" = model.late),
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estimate = c("{estimate}", "{stars}{std.error}", "{estimate}", "{stars}{std.error}"),
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statistic = NULL,
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gof_omit = ".*",
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coef_rename = c(
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"sum" = "(Sum)",
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"diff.sum0.h-t.accounts" = "Smaller server",
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"nodeocov.sum.accounts" = "Server size\n(outgoing)",
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"nodeifactor.sum.registrations.TRUE" = "Open registrations\n(incoming)",
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"nodematch.sum.language" = "Languages match"
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),
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align="lrrrr",
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stars = c('*' = .05, '**' = 0.01, '***' = .001),
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output = format
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#output = "markdown",
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#table.envir='table*',
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#table.env="table*"
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) %>% add_header_above(c(" " = 1, "Model A" = 2, "Model B" = 2))
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if (knitr::is_latex_output()) {
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x %>% reduce(str_c, capture.output(.), sep="\n") %>% gsub("table", "table*", .) %>% knitr::raw_latex()
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} else {
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x
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}
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```
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# Proposed Recommendation System
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_How can we build an opt-in, low-resource recommendation system for finding Fediverse servers?_
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Tailored servers focused on a particular topic and community have advantages for onboarding newcomers; however, it may be difficult for new and existing Mastodon users to discover these communities. To address this gap, we propose a recommendation system for finding new servers. This system would be opt-in and low-resource, requiring only a small amount of data from each server.
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First, we construct the ideal system based on observted data. That is, we use the data from all posts we collected from all servers to construct an ideal recommender. We then simulate various scenarios that limit both servers that report data and the number of tags they report. We use rank biased overlap (RBO) to then compare the outputs from these simulations to the baseline with more complete information from all tags on all servers.
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## Recommendation System Design
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We use a term frequency-inverse document frequency model to associate the top tags with each server. For the term frequency, we divide the count of the number of accounts which used the tag during the six-month period by the total number of known account-tag pairs on that server; for the inverse document frequency, we divide the total number of servers by count of servers reporting the tag. In this implimentation, we also apply filters such that tags must be used to at least five people on the server to be reported and the tag must be used by at least ten people and at least three servers in the entire known system.
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## Rubustness to Limited Data
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```{r}
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#| label: fig-simulations-rbo
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#| fig-env: figure*
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#| cache: true
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#| fig-width: 6.75
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#| fig-height: 3
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#| fig-pos: tb
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library(tidyverse)
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library(arrow)
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simulations <- arrow::read_ipc_file("data/scratch/simulation_rbo.feather")
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simulations %>%
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group_by(servers, tags, run) %>% summarize(rbo=mean(rbo), .groups="drop") %>%
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mutate(ltags = as.integer(log2(tags))) %>%
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ggplot(aes(x = factor(ltags), y = rbo, fill = factor(ltags))) +
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geom_boxplot() +
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facet_wrap(~servers, nrow=1) +
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#scale_y_continuous(limits = c(0, 1)) +
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labs(x = "Tags (log2)", y = "RBO", title = "Rank Biased Overlap with Baseline Rankings by Number of Servers") +
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theme_minimal() + theme(legend.position = "none")
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```
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We simulated various scenarios that limit both servers that report data and the number of tags they report. We used rank biased overlap (RBO) to then compare the outputs from these simulations to the baseline with more complete information from all tags on all servers. @fig-simulations-rbo shows how the average agreement with the baseline scales linearly with the logarithm of the tag count.
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# Conclusion
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Based on analysis of trace data from millions of new Fediverse accounts, we find evidence that suggests that servers matter and that users tend to move from larger servers to smaller servers. We then propose a recommendation system that can help new Fediverse users find servers with a high probability of being a good match based on their interests. Based on simulations, we demonstrate that such a tool can be effectively deployed in a federated manner, even with limited data on each local server.
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||
# References {#references}
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||
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||
::: {.content-visible when-format="html"}
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# Appendix {#appendix .appendix}
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## Push and Pull Model
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{{< include notebooks/_push_pull.qmd >}}
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::: |