261 lines
5.6 KiB
Plaintext
261 lines
5.6 KiB
Plaintext
---
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title: "Do Servers Matter on Mastodon?"
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subtitle: "Data-driven Design for Decentralized Social Media"
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author: "Carl Colglazier"
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institute:
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- "Community Data Science Collective"
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- "Northwestern University"
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date: "2024-03-08"
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title-slide-attributes:
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data-background: "#4c3854"
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format:
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revealjs:
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width: 1600
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height: 900
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date-format: long
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margin: 0.2
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center-title-slide: false
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#disable-layout: true
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theme: [default, presentation.scss]
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slide-number: false
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keep-md: true
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pdf-max-pages-per-slide: 1
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template-partials:
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- title-slide.html
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# beamer:
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# aspectratio: 169
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# theme: metropolis
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# colortheme: seahorse
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knitr:
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opts_chunk:
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dev: "ragg_png"
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retina: 1
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dpi: 300
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execute:
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freeze: auto
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cache: true
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echo: false
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# fig-width: 5
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# fig-height: 6
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prefer-html: true
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---
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## Empirical Context
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::: {.columns}
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:::: {.column}
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- **The Fediverse**: A set of decentralized online social networks which interoperate using shared protocols like ActivityPub.
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- **Mastodon**: An open-source, decentralized social network and microblogging community.
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::::
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:::: {.column}
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::::
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:::
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# 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}
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## Mastodon grew significantly in 2022 and 2023
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```{r}
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#| label: fig-account-timeline
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#| fig-width: 5
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#| fig-height: 2.5
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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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## Which server should I join?
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### Conflicting advice
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::: {.columns}
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::: {.column}
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Just join any server!
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:::
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::: {.column}
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Join the _right_ server!
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:::
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:::
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::: {.fragment}
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### Which is right? {.center-xy}
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:::
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---
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{.center}
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# Does server choice matter? {.center}
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## Survival model for new accounts
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Are they more likely to stay active after 91 days.
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::: {.columns}
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::: {.column}
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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: 2.25
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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_km
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```
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:::
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::: {.column .small}
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```{r}
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#| label: tbl-coxme
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library(ehahelper)
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library(broom)
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cxme_table <- tidy(cxme) %>%
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mutate(conf.low = exp(conf.low), conf.high=exp(conf.high)) %>%
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mutate(term = case_when(
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term == "factor(group)1" ~ "Join Mastodon",
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term == "factor(group)2" ~ "General Servers",
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term == "small_serverTRUE" ~ "Small Server",
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TRUE ~ term
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)) %>%
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mutate(exp.coef = paste("(", round(conf.low, 2), ", ", round(conf.high, 2), ")", sep="")) %>%
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select(term, estimate, exp.coef , p.value)
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cxme_table %>% knitr::kable(digits = 3)
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```
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:::
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:::
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## Accounts that move
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Do they move to larger servers or to smaller servers?
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::: {.small}
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```{r}
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#| label: tbl-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_tabular"
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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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"nonzero" = "Nonzero",
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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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) %>% add_header_above(c(" " = 1, "Model A" = 2, "Model B" = 2))
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x
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```
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:::
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# Our analysis suggests {.center}
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- Accounts on large, general servers fare worse
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- Moved accounts go to smaller servers
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Can we build a system that helps people find servers?
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# Recommendation System Concept
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- Report top **hashtags** used by the most accounts on each server
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- Build an $M \times N$ server-tag matrix
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- Normalize with Okai BM25 TF-IDF and L2 normalization
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::: {.fragment}
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Using this matrix, we can
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- Calculate similarity between servers using tags
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- Calculate similarity between tags using servers
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- Reccommend servers based on affinity toward certain tags
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:::
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## Example: Server Similarity
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::: {#tbl-sim-servers}
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```{r}
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#| label: table-sim-servers
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library(tidyverse)
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library(arrow)
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library(here)
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sim_servers <- here("data/scratch/server_similarity.feather") %>% arrow::read_ipc_file()
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server_of_interest <- "hci.social"
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server_table <- sim_servers %>%
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arrange(desc(Similarity)) %>%
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filter(Source == server_of_interest | Target == server_of_interest) %>%
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head(7) %>%
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pivot_longer(cols=c(Source, Target)) %>%
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filter(value != server_of_interest) %>%
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select(value, Similarity) %>%
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rename("Server" = "value")
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if (knitr::is_latex_output()) {
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server_table %>% knitr::kable(format="latex", booktabs=TRUE, digits=3)
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} else {
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server_table %>% knitr::kable(digits = 3)
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}
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```
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Top five servers most similar to hci.social
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:::
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# Future Work
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- Evaluation of the recommendation system
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- More specific analysis of account attributes
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- Simulations for robustness
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