A social and behavioural research group at Western Sydney University is studying social activists. They have consulted you to investigate the flow of information regarding environmental activist Greta Thunberg on Twitter. Researchers have provided...

As an AI language model, I cannot perform tasks that involve accessing Twitter data or any other personal information. However, I can provide a general outline of how one could approach this analysis using R with the rtweet and igraph libraries:

  1. Followed by Greta:
  • Use the get_followers() function in rtweet to get a list of Greta’s followers.
  • Filter out any company twitter handles from the list.
  • Use the lookup_users() function to get more information about each user, including their number of followers.
  • Sort the list by number of followers and select the top 12 users.
  • Examine each user’s twitter account to summarise their type (e.g., activist, politician, celebrity).
  1. Followers of Greta:
  • Use the get_friends() function in rtweet to get a list of users followed by Greta.
  • Use the get_followers() function to get a list of users who follow Greta.
  • Find the overlap between these two lists to get a list of users who both follow Greta and are followed by her.
  • For each user in this list, use the search_tweets() function to search for tweets containing both their username and “Greta Thunberg”.
  • Analyse these tweets to determine if they have a positive or negative relationship with Greta (e.g., supportive, critical).
  • Examine each user’s twitter account to summarise their type.
  1. Bypassing Greta:
  • Use the lists obtained in step 1 and 2 above.
  • Create an empty graph object using the igraph library.
  • Add nodes for each user in these lists to the graph object.
  • Use the get_friends() function again to find if any following or followers are friends with each other on Twitter and add those edges to the graph object as well (using add_edges()).
  • Manually determine if any of the following and followers should be friends, based on their background, and add those edges to the graph object as well.
  • Plot the resulting graph.
  1. Graph Statistics:
  • Use the igraph library to compute the diameter (maximum distance between any two nodes) and density (proportion of edges present in relation to all possible edges) of the graph object.
  • Calculate neighbourhood overlap of each edge using neighborhood_overlap() function.
  • Identify which nodes have highest degree centrality (i.e., social capital).
  • Analyse whether these results are obvious from the graph structure or not.
  1. Graph Homophily:
  • Label each node in the graph as either a supporter or non-supporter of Greta based on information obtained in steps 1, 2 and 3.
  • Write hypotheses for homophily analysis, such as “supporters are more likely to follow/support other supporters than non-supporters”.
  • Compute appropriate test statistic (e.g., chi-square test) and significance level to test these hypotheses.
  • Draw conclusions based on the results.
  1. Structural Balance:
  • Label existing edges as positive or negative based on their association with Greta (e.g., friendliness towards her).
  • Use hierarchical clustering algorithm in igraph to identify clusters within signed network.
  • Analyse relationships within/between these clusters to identify potential sources of imbalance.


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