Create a directed network of Twitter followers. Based on https://shkspr.mobi/blog/2015/03/this-is-what-a-graph-of-8000-fake-twitter-accounts-looks-like/

These scripts work in three parts.

1. Taking an initial user, download information about who they follow. Repeat recursively.
2. Generate a directed graph.
3. Draw an image of the graph.

• Choose the user you wish to track - for example @edent.

• Decide what recursive depth you want to go. A depth of 1 or 2 should be done in a few hours (depending on how many people they are following), a depth of 5 can take several days.

  python GetFollowing.py -s edent -d 2

This will generate a directory structure like

.
├── following
│   ├── edent.csv
│   ├── alice.csv
│   ├── bob.csv
│   └── carol.csv
└── twitter-users
    ├── 3104869030.json
    ├── 3105479302.json
    ├── 3111045413.json
    └── 3112012750.json

The following directory is contains the Twitter Usernames. Each is a .csv file showing who they are following.

The twitter-users directory contains a .json representation of each user. The file name is their Twitter ID.

This script parses the .csv files and creates a new .csv which contains the Following graph.

python GenerateNetwork.py -s edent

The file twitter_network.csv contains a comma delimited graph

3112012750,3111045413,1
3111045413,3111252693,2

Column 1 is the Twitter ID of a User. Column 2 is the ID of a User they follow. Column 3 is the number of followers the User has.

If you want to create a visual representation, you can import twitter_network.csv into your favourite stats package. Or, you can run

python DrawGraph.py

Some scripts based on http://mark-kay.net/2014/08/15/network-graph-of-twitter-followers/

With permission granted from the original author to adapt https://twitter.com/markleekay/status/574362042204815361

For more information, please see https://shkspr.mobi/blog/2015/03/this-is-what-a-graph-of-8000-fake-twitter-accounts-looks-like/