Lightning talk presentation.

presentations/images

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+../images

presentations/lightning_talk.qmd

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+---
+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

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