---
title: "Do Servers Matter on Mastodon?"
subtitle: "Data-driven Design for Decentralized Social Media"
author: "Carl Colglazier"
institute: 
  - "Community Data Science Collective"
  - "Northwestern University"
date: "2024-03-08"
title-slide-attributes:
  data-background: "#4c3854"
format: 
  revealjs:
    width: 1600
    height: 900
    date-format: long
    margin: 0.2
    center-title-slide: false
    #disable-layout: true
    theme: [default, presentation.scss]
    slide-number: false
    keep-md: true
    pdf-max-pages-per-slide: 1
    template-partials:
      - title-slide.html
#  beamer:
#    aspectratio: 169
#    theme: metropolis
#    colortheme: seahorse
knitr:
  opts_chunk:
    dev: "ragg_png"
    retina: 1
    dpi: 300
execute:
  freeze: auto
  cache: true
  echo: false
#  fig-width: 5
#  fig-height: 6
prefer-html: true
---

## Empirical Context

::: {.columns}

:::: {.column}

- **The Fediverse**: A set of decentralized online social networks which interoperate using shared protocols like ActivityPub.

- **Mastodon**: An open-source, decentralized social network and microblogging community.

::::

:::: {.column}

![A screenshot of Mastodon 2.9 (2019), from the Mastodon Blog.](images/Mastodon_Single-column-layout.png)

::::

:::

# The Fediverse is a network of _thousands_ of interconnected servers {background-color="black" data-background-image="images/mastodon_map.png" background-repeat="repeat" background-size="200px" background-opacity="0.5" .center auto-animate=true .fade-out}

## Mastodon grew significantly in 2022 and 2023

```{r}
#| label: fig-account-timeline
#| fig-width: 5
#| fig-height: 2.5
library(here)
source(here("code/helpers.R"))
account_timeline_plot()
```

## Which server should I join?

### Conflicting advice

::: {.columns}
::: {.column}

Just join any server! 

:::
::: {.column}

Join the _right_ server!

:::
:::

::: {.fragment}
### Which is right? {.center-xy}
:::

---

![](images/joinmastodon-screenshot.png){.center}

# Does server choice matter? {.center}

## Survival model for new accounts

Are they more likely to stay active after 91 days.

::: {.columns}

::: {.column}

```{r, cache.extra = tools::md5sum("code/survival.R")}
#| cache: true
#| label: fig-survival
#| fig-env: figure
#| fig-cap: "Survival probabilities for accounts created during May 2023."
#| fig-width: 3.375
#| fig-height: 2.25
#| fig-pos: h!

library(here)
source(here("code/survival.R"))
plot_km 
```

:::

::: {.column .small}

```{r}
#| label: tbl-coxme
library(ehahelper)
library(broom)

cxme_table <- tidy(cxme) %>%
  mutate(conf.low = exp(conf.low), conf.high=exp(conf.high)) %>%
  mutate(term = case_when(
    term == "factor(group)1" ~ "Join Mastodon",
    term == "factor(group)2" ~ "General Servers",
    term == "small_serverTRUE" ~ "Small Server",
    TRUE ~ term
  )) %>%
  mutate(exp.coef = paste("(", round(conf.low, 2), ", ", round(conf.high, 2), ")", sep="")) %>%
  select(term, estimate, exp.coef , p.value)

cxme_table %>% knitr::kable(digits = 3)
```

:::

:::

## Accounts that move

Do they move to larger servers or to smaller servers?

::: {.small}

```{r}
#| label: tbl-ergm-table
#| echo: false
#| warning: false
#| message: false
#| error: false

library(here)
library(modelsummary)
library(kableExtra)
library(purrr)
library(stringr)
load(file = here("data/scratch/ergm-model-early.rda"))
load(file = here("data/scratch/ergm-model-late.rda"))

if (knitr::is_latex_output()) {
  format <- "latex_tabular"
} else {
  format <- "html"
}

x <- modelsummary(
  list("Coef." = model.early, "Std.Error" = model.early, "Coef." = model.late, "Std.Error" = model.late),
  estimate = c("{estimate}", "{stars}{std.error}", "{estimate}", "{stars}{std.error}"),
  statistic = NULL,
  gof_omit = ".*",
  coef_rename = c(
    "sum" = "Sum",
    "nonzero" = "Nonzero",
    "diff.sum0.h-t.accounts" = "Smaller server",
    "nodeocov.sum.accounts" = "Server size\n(outgoing)",
    "nodeifactor.sum.registrations.TRUE" = "Open registrations\n(incoming)",
    "nodematch.sum.language" = "Languages match"
  ),
  align="lrrrr",
  stars = c('*' = .05, '**' = 0.01, '***' = .001),
  output = format
  ) %>% add_header_above(c(" " = 1, "Model A" = 2, "Model B" = 2))

x
```

:::

# Our analysis suggests {.center}

- Accounts on large, general servers fare worse
- Moved accounts go to smaller servers

Can we build a system that helps people find servers?

# Recommendation System Concept

- Report top **hashtags** used by the most accounts on each server
- Build an $M \times N$ server-tag matrix
- Normalize with Okai BM25 TF-IDF and L2 normalization


::: {.fragment}
Using this matrix, we can

- Calculate similarity between servers using tags
- Calculate similarity between tags using servers
- Reccommend servers based on affinity toward certain tags
:::

## Example: Server Similarity

::: {#tbl-sim-servers}

```{r}
#| label: table-sim-servers
library(tidyverse)
library(arrow)
library(here)

sim_servers <- here("data/scratch/server_similarity.feather") %>% arrow::read_ipc_file() 
server_of_interest <- "hci.social"
server_table <- sim_servers %>%
    arrange(desc(Similarity)) %>%
    filter(Source == server_of_interest | Target == server_of_interest) %>%
    head(7) %>%
    pivot_longer(cols=c(Source, Target)) %>%
    filter(value != server_of_interest) %>%
    select(value, Similarity) %>%
    rename("Server" = "value")

if (knitr::is_latex_output()) {
  server_table %>% knitr::kable(format="latex", booktabs=TRUE, digits=3)
} else {
  server_table %>% knitr::kable(digits = 3)
}
```

Top five servers most similar to hci.social

:::

# Future Work

- Evaluation of the recommendation system
- More specific analysis of account attributes
- Simulations for robustness