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You can retrieve a list of followers and those followed by a user
through the get_followers
and get_follows
functions, respectively. This gives a glimpse of their social dynamics
on the platform.
get_followers
Retrieve the followers of a specific account with
get_followers
:
get_followers(actor = "benguinaudeau.bsky.social", limit = 200) |>
dplyr::glimpse()
#> Rows: 200
#> Columns: 8
#> $ did <chr> "did:plc:nol67uyam6hzj5…
#> $ indexed_at <dttm> 2024-02-29 13:50:12, 2…
#> $ actor_handle <chr> "dkarpa.bsky.social", "…
#> $ actor_name <chr> "David Karpa", "Rémi Pa…
#> $ actor_description <chr> "PhD-candidate UBremen.…
#> $ actor_avatar <chr> "https://cdn.bsky.app/i…
#> $ viewer_data <list> [FALSE, FALSE], [FALSE…
#> $ labels_data <list> [], [], [], [], [], []…
get_follows
Retrieve the accounts that are followed by a specific account with
get_follows
:
get_follows(actor = "benguinaudeau.bsky.social", limit = 200) |>
dplyr::glimpse()
#> Rows: 174
#> Columns: 8
#> $ did <chr> "did:plc:3ryku2cbgpazor…
#> $ indexed_at <dttm> 2024-02-08 05:16:04, 2…
#> $ actor_handle <chr> "beamagistro.bsky.socia…
#> $ actor_name <chr> "Beatrice Magistro", "S…
#> $ actor_description <chr> "PostDoc @Caltech, thru…
#> $ actor_avatar <chr> "https://cdn.bsky.app/i…
#> $ viewer_data <list> [FALSE, FALSE], [FALSE…
#> $ labels_data <list> [], [], [], [], [], []…
You’ll notice each follower/following has a description (i.e. their bio). It’s a nifty way to get to know a bit about them at a glance!
This section guides you through the process of visualizing a follower network for a given user on Blue Sky. The primary focus is on the interconnected relationships among followers.
The first step is to load some additional packages. Then we get the followers for the main user we are interested in. We will then delve deeper and fetch the followers for each of these followers.
library(ggplot2)
library(dplyr)
library(purrr)
library(tidyr)
library(igraph)
library(ggraph)
library(tidygraph)
# Retrieve the followers for the main user
some_followers <- get_followers(actor = "benguinaudeau.bsky.social", limit = 10)$actor_handle
# For each follower, retrieve their own set of followers.
# This provides a nested view of relationships.
followers_of_followers <- some_followers |>
purrr::map_dfr(~{
get_followers(actor = .x, limit = 200) |>
mutate(from = .x)
}) |>
dplyr::rename(to = actor_handle) |>
dplyr::select(from, to) %>%
dplyr::add_count(to, name = "n_to") %>%
dplyr::add_count(from, name = "n_from") %>%
dplyr::filter(n_to > 1 | n_from > 1) %>%
dplyr::select(-n_to:-n_from)
# Construct the network graph and plot
graph <- tidygraph::as_tbl_graph(followers_of_followers, directed = TRUE)
# Use ggraph to visualize the network.
ggraph::ggraph(graph, layout = 'fr') +
ggraph::geom_edge_link() +
ggraph::geom_node_point(aes(size = tidygraph::centrality_pagerank()), color = "lightblue") +
ggraph::geom_node_text(aes(label = name, size = tidygraph::centrality_pagerank(), family = "mono", fontface = "bold"),
vjust = 1, hjust = 1, check_overlap = T, color = "white", show.legend = F) +
ggraph::geom_node_text(aes(label = name, size = tidygraph::centrality_pagerank(), family = "mono"),
vjust = 1, hjust = 1, check_overlap = T, color = "blue", show.legend = F) +
ggplot2::theme_void() +
ggplot2::theme(legend.position = "bottom")
You can use a similar approach to find new people you might want to follow. First get all the people you already follow by quering your own account:
my_follows <- get_follows(actor = "jbgruber.bsky.social",
limit = 10)$actor_handle # limit only for demonstration
# For each account you follow, retrieve who they follow.
follows_of_follows <- my_follows |>
purrr::map_dfr(~{
get_followers(actor = .x, limit = 100) |>
mutate(from = .x)
})
# Now we can check which accounts are popular among the people you already follow
follows_of_follows |>
dplyr::filter(!actor_handle %in% my_follows) |> # exclude accounts you already follow
dplyr::count(actor_name, actor_handle, sort = TRUE)
#> # A tibble: 704 × 3
#> actor_name actor_handle n
#> <chr> <chr> <int>
#> 1 Johannes B. Gruber jbgruber.bsky.s… 9
#> 2 Center for Ballot Freedom fusionvoting.bs… 4
#> 3 David Karpa dkarpa.bsky.soc… 3
#> 4 Jacob Ware jacobware.bsky.… 3
#> 5 Jens Rydgren jensrydgren.bsk… 3
#> 6 edminkardes gorkem.me 3
#> 7 Adrian Florea aflorea.bsky.so… 2
#> 8 Brian O'Meara omearabrian.bsk… 2
#> 9 Darren Dahly statsepi.bsky.s… 2
#> 10 Erik Angner erikangner.com 2
#> # ℹ 694 more rows
The original account shows up in this overview, as do accounts that were excluded because of the limit set to do the demonstration.
But all of these accounts look interesting!
You can also retrieve the accounts that liked or reposted your skeets.
Want to create like networks? The get_likes
function
allows you to extract information about users who liked a specific post.
By simply providing the post’s URL, you can get a list of users who
expressed their appreciation for the content.
get_likes("https://bsky.app/profile/ryanlcooper.com/post/3kb42gayda52t") |>
dplyr::glimpse()
#> Rows: 25
#> Columns: 5
#> $ created_at <dttm> 2023-10-06 21:22:39, 2023-1…
#> $ indexed_at <dttm> 2023-10-06 21:22:39, 2023-1…
#> $ actor_handle <chr> "csehested.bsky.social", "da…
#> $ actor_name <chr> NA, "TassieDevyl", "Hans LeJ…
#> $ actor_data <list> ["did:plc:zzsj7hy3wec6fhbwu…
Want to create repost networks? Reposts (or reskeets), akin to retweets on platforms like Twitter, amplify the reach of content by sharing it with a wider audience.
The get_reposts
function is designed to retrieve
information about users who reposted a specific piece of content.
get_reposts(post_url = "https://bsky.app/profile/ryanlcooper.com/post/3kb42gayda52t") |>
dplyr::glimpse()
#> Rows: 25
#> Columns: 8
#> $ did <chr> "did:plc:zyvmwfzbkt3igd…
#> $ indexed_at <dttm> 2024-01-26 00:03:39, 2…
#> $ actor_handle <chr> "zunguzungu.bsky.social…
#> $ actor_name <chr> "Aaron Bady", "Bertie R…
#> $ actor_description <chr> "Meat blood, bees, thin…
#> $ actor_avatar <chr> "https://cdn.bsky.app/i…
#> $ viewer_data <list> [FALSE, FALSE], [FALSE…
#> $ labels_data <list> [], [], [], [], [], []…
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.