A scraper written for my reseach project on civic hacking.
Python
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README.md
config.json
github_scraper.py
organizations.txt
requirements.txt

README.md

GitHub scraper

This repo contains a GitHub scraper I wrote for my research project on civic hacking. At the moment it is very much focused on organizations and their members, i.e. it only takes a list or organizations as its input, not normal user accounts. It generates CSV spreadsheets or GEXF files to be used in network analysis software like Gephi. I tested it with Python 2.7.10 and 3.5.0, but I recommend using Python 3.

The scraper can do the following:

  1. Get a list of the organizations' repositories (CSV).
  2. Get all contributors of the organizations' repositories as a spreadsheet and as a directed graph (CSV and GEXF).
  3. Get a list of the repositories of all the members of the organizations (CSV).
  4. Get information for each member of the organizations (CSV).
  5. Get a list of all the repositories starred by the members of the organizations (CSV).
  6. Generate a 'full' follower network. This will take all the members of the organizations and collect all their followers and all the users they are following. Will create a directed graph (GEXF).
  7. Generate a 'narrow' follower network. Works like 6 but only collects connections between the members of the organizations scraped by the user (GEXF).
  8. Generate a directed graph illustrating the membership structures (GEXF).

How to use

  1. Open config.json and add your GitHub user name and your personal access token to access the GitHub API.
  2. Open organizations.txt and add the user account names of the organizations you want to scrape -- one organization per line! For example, if you want to scrape mySociety, Open Knowledge, and Ushahidi, your list will look like this:
mysociety
okfn
ushahidi
  1. Start the scraper with python github_scaper.py and choose an option. You can perform several scrapes in one run by entering several numbers separated by commas (, ). The results will be stored in the data subfolder.

If you cannot run the script, required packages might be missing. Install via [sudo] pip install -r requirements.txt.