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.
- Taking an initial user, download information about who they follow. Repeat recursively.
- Generate a directed graph.
- 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/
