This interactive demonstration shows some of the ways in which data from the WikiWho API (together with other sources) can be used to explore article editing dynamics as well as shed light on the edit history of specific users.
Particuarly, the notebooks it contains help in determining and expressing the impact of a Wikipedia user regarding her participation or production of knowledge in articles. The visualizations provided can be used to study the outcomes of various editing dynamics on the articles and by playing with parameters and metrics we have defined and explain, the user can uncover new patterns.
No coding skills needed, but code is transparent
The demo needs no programming knowledge to be used, but offers transparency by simply masking code behind Jupyter's "App mode", allowing more experienced users to dive into the mechanics of the API calls and calculations behind it and immediately modfiy it for their own purpose.
All data used comes from live systems (Wikipedia directly or Wikiwho) for the English languge version of Wikipedia, reflecting the current article status.
Getting Started with this Demo
Use the Binder link to run the notebooks. Then select the article you want to analyse. First explore the article; go through the notebooks (which will open in different tabs in your browser) in order and you will be further on be able to also inspect users more in details. You can switch between the various notebooks.
For a local installation you're going to need:
- Windows, Linux or OS X
- Python version 3.6
- Jupyter Notebook Framework
Need Help? Found a bug?
Submit the issue to GitHub if you find one, and feel free to submit pull requests with bug fixes or changes.
- Dr. Fabian Flöck (@faflo)