Graph Tweets

library(tidyverse)
library(DT)
library(igraph)
library(rtweet)
library(visNetwork)
library(graphTweets)            

1. Here are the last 300 tweets from each member of the US Senate twitter list.

senate <- lists_members(slug = "us-senate", owner_user = "TwitterGov")

senate_tweets <- get_timeline(senate$screen_name, n = 300)

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Ta-Da.

2. Here is a network of retweets for the US Senate twitter list.

senate_retweets_network <- senate_tweets %>% 
  filter(retweet_screen_name %in% senate$screen_name) %>%    
  gt_edges(screen_name, retweet_screen_name, text) %>%            
  gt_graph()



senate_retweets_nodes <- as_data_frame(senate_retweets_network, what = "vertices")


senate_retweets_nodes <- senate_retweets_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(senate_retweets_network)) %>% 
  mutate(value = degree)


senate_retweets_edges <- as_data_frame(senate_retweets_network, what = "edges")


senate_retweets_edges <- senate_retweets_edges %>% 
  mutate(title = text)


visNetwork(senate_retweets_nodes, senate_retweets_edges, main = "US Senate officials retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")

There are quite a few outliars from this network.

3. Here is a count of nodes and edges, and get the density of the network.

senate_retweets_network %>% 
  vcount()

[1] 170

senate_retweets_network %>% 
  ecount()

[1] 1631

senate_retweets_network %>% 
  edge_density()

[1] 0.05676993

This network’s density is less than one tenth.

4. Here is a data table of the list members, showing only their names and degree.

senate_retweets_nodes %>%
  select(name,degree)

This Twitter list has 170 members which is a lot.

5. Here are the communities shown in a diagram.

senate_retweets_nodes <- senate_retweets_nodes %>% 
  mutate(group = membership(infomap.community(senate_retweets_network)))

Modularity is implemented for undirected graphs only.

visNetwork(senate_retweets_nodes, senate_retweets_edges, main = "US Senate officials retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to") %>%   
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")

NA
NA

Orange appears to be the largest community in this network.