create_edgelist()
Another useful approach to social media research is social
network analysis. Getting started with social network analysis is
as simple as producing an edgelist, a two-column
dataframe listing senders and receivers. An edgelist
gives a complete accounting of whom is interacting with whom. In
Twitter, this is complicated somewhat by the number of ways a user is
able to interact with someone else, namely, through replying,
retweeting, quote tweeting, mentioning, and liking tweets. The
tidytags function create_edgelist() uses
filter_by_tweet_type() to create an edgelist that takes
into account three of these types of interaction.
create_edgelist() returns a dataframe with three columns:
two for the sender and receiver Twitter handles, and a third column
listing the tweet type (i.e., the form of interaction). The default is
for create_edgelist() to create an edgelist of all possible
interactions, but focusing on one type of interaction is easily
accomplished as well (e.g., looking at interactions through replies
using create_edgelist(df, "reply")).
Run create_edgelist() after completing
get_upstream_tweets() and process_tweets() for
a complete picture of the interactions.
example_edgelist <-
create_edgelist(process_tweets(example_with_upstream))
head(example_edgelist, 20)
#> tweet_type sender receiver
#> 1 reply nicolapallitt eromerohall
#> 2 reply jeroen69 vdennen
#> 3 reply jmenglund03 bonni208
#> 4 reply DKSch DKSch
#> 5 reply DKSch DKSch
#> 6 reply _valeriei eromerohall
#> 7 reply jmenglund03 SBarksway
#> 8 reply DrTerriC mete_akca
#> 9 reply pazureka SBarksway
#> 10 reply pazureka SBarksway
#> 11 reply TAmankwatia TAmankwatia
#> 12 reply PaulineMuljana robmoore3
#> 13 reply yinbk DrVirtuality
#> 14 reply arasbozkurt arasbozkurt
#> 15 reply arasbozkurt arasbozkurt
#> 16 reply arasbozkurt eromerohall
#> 17 reply correia65 AnnaRoseLeach
#> 18 reply jmenglund03 PaulineMuljana
#> 19 reply PaulineMuljana AmyLomellini_ID
#> 20 reply teacherrogers teacherrogersWe can then easily visualize the edgelist as a sociogram using the tidygraph and ggraph packages.
First, create a graph object using tidygraph:
if (requireNamespace("tidygraph", quietly = TRUE)) {
example_graph <-
tidygraph::as_tbl_graph(example_edgelist)
example_graph <-
dplyr::mutate(example_graph,
popularity = tidygraph::centrality_degree(mode = 'in'))
}Then plot using ggraph:
if (requireNamespace("ggraph", quietly = TRUE) &
requireNamespace("ggplot2", quietly = TRUE)
) {
ggraph::ggraph(example_graph, layout = 'kk') +
ggraph::geom_edge_arc(alpha = .2,
width = .5,
strength = .5,
edge_colour = 'steelblue'
) +
ggraph::geom_node_point(alpha = .4, ggplot2::aes(size = popularity)) +
ggplot2::scale_size(range = c(1,10))
}
Running create_edgelist() also provides a simple way to
re-look at how many tweets of each type are present in the dataset,
using the count() function from dplyr.
dplyr::count(example_edgelist, tweet_type, sort = TRUE)
#> tweet_type n
#> 1 retweet 1146
#> 2 quote 108
#> 3 reply 71Note that create_edgelist() does not yet accept
type = "mention" or type = "like" parameters
due to the complicated ways mentions are included in tweets as well as
limitations of the information provided by the Twitter API.