MT News

1. For this assignment the twitter list being analyzed is MT News, a list of Montana news sources. The code below gathers the last 200 tweets from the members of the list.

mt_news <- lists_members(slug = "MT-News", owner_user = "jacobmspringer")
mt_news

mt_news <- get_timeline(mt_news$screen_name, n = 200)

2. This code gathers the node and edge data for MT News and uses it to create a network of retweets. It also creates a diagram of the network.

mt_retweet_network <- mt_news %>% 
  filter(retweet_screen_name %in% mt_news$screen_name) %>% 
  gt_edges(screen_name, retweet_screen_name, text) %>% 
  gt_graph

mt_retweet_nodes <- as_data_frame(mt_retweet_network, what = "vertices")

mt_retweet_edges <- as_data_frame(mt_retweet_network, what = "edges")

mt_retweet_nodes <- mt_retweet_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(mt_retweet_network)) %>% 
  mutate(value = degree)

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

visNetwork(mt_retweet_nodes, 
           mt_retweet_edges,
           main = "Montana News Source Twitter Retweet Network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")

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3. This code gets the number of nodes in the network.

mt_retweet_network %>% 
  vcount()

[1] 33

This code gets the number of edges in the network.

mt_retweet_network %>%
  ecount()

[1] 280

This code gets the density of the network.

mt_retweet_network %>%
  edge_density()

[1] 0.2651515

4. This code creates a datatable of the names and degrees of members in the MT News list.

mt_retweet_nodes %>%
  select(name,degree) %>%
  datatable()

5. Like before, this code finds the nodes and edges of the MT News data and organizes it into a diagram.

mt_retweet_network <- mt_news %>% 
  filter(retweet_screen_name %in% mt_news$screen_name) %>%     
  gt_edges(screen_name, retweet_screen_name, text) %>%        
  gt_graph()

mt_retweet_nodes <- as_data_frame(mt_retweet_network, what = "vertices")

mt_retweet_nodes <- mt_retweet_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(mt_retweet_network)) %>% 
  mutate(value = degree)

mt_retweet_edges <- as_data_frame(mt_retweet_network, what = "edges")

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

visNetwork(mt_retweet_nodes, mt_retweet_edges, main = "Montana News Source Twitter Retweet Network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")

The code below groups the retweet data together into communities to better see how the members interact.

mt_retweet_nodes <- mt_retweet_nodes %>% 
  mutate(group = membership(infomap.community(mt_retweet_network)))

Modularity is implemented for undirected graphs only.

visNetwork(mt_retweet_nodes, mt_retweet_edges, main = "Montana News Source Twitter Retweet Network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to") %>%   
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")

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