Wikimedia ORES

Data acquisition

Two data sources are used: (1) Wikipedia articles of politicians and (2) world population data.

Wikipedia articles - The Wikipedia articles can be found on Figshare. It contains politiciaans by country from the English-language wikipedia. The already downloaded dataset can be found in Wiki articles

Population data - This dataset is drawn from the world population datasheet published by the Population Reference Bureau (downloaded 2020-11-13 10:14 AM). The already downloaded dataset can be found in World population data

Getting article quality predictions with ORES

The predicted quality scores for each article in the Wikipedia dataset are accessed via a machine learning system called ORES ("Objective Revision Evaluation Service"). ORES estimates the quality of an article (at a particular point in time), and assigns a series of probabilities that the article is in one of the six quality categories. The options are, from best to worst:

ID	Quality Category	Explanation
1	FA	Featured article
2	GA	Good article
3	B	B-class article
4	C	C-class article
5	Start	Start-class article
6	Stub	Stub-class article

ORES REST API endpoint

The ORES REST API is used to access ORES scores. It expects the following parameters:

Getting started

The "Amazing Python Data Workflow with Poetry, Pandas, and Jupyter"¹ is used for this project.

Prerequisites

In order to take part in the course, please ensure that you have a Python version greater or equal to 3.6.1, a working installation of Poetry and git installed.

Setup

1. Clone this repository (or use SSH) and move it into the repo root.

   git clone https://github.com/Francosinus/A3-hcds-hcc.git cd A3-hcds-hcc

2. Install the dependencies in the repo root.

   poetry install

3. Create a subshell within the virtual environment by running:

   poetry shell

4. Open the project with Jupyter in your browser.

   jupyter notebook

Dependencies:

python = "^3.6.1"  
pandas = "^1.1.3"  
jupyter = "^1.0.0"  
ipykernel = "^5.3.4"  
requests = "^2.24.0"  
matplotlib = "^3.3.2"  
seaborn = "^0.11.0"  
altair = "^4.1.0"  
bs4 = "^0.0.1"  
xlrd = "^1.2.0"

Analysis

The analysis consists of calculating the proportion (as a percentage) of articles-per-population and high-quality articles for each country and for each region. By "high quality" arcticle an article that ORES predicted as FA (featured article) or GA (good article) is meant.

Examples:

• if a country has a population of 10,000 people, and you found 10 articles about politicians from that country, then the percentage of articles-per-population would be 0.1%.
• if a country has 10 articles about politicians, and 2 of them are FA or GA class articles, then the percentage of high-quality-articles would be 20%.

The final atbles can be found in the folder Results.

Troubleshooting

• Problems when installing poetry? When installing poetry something goes wrong. It's not automatically in your path, so if you run poetry --version nothing happens. If you use zsh or oh-my-zsh then you need to add the following line to your .zshrc file export PATH="$HOME/.poetry/bin:$PATH.

• Trouble with previewing notebooks directly in GitHub? --> https://nbviewer.jupyter.org/

---

[1] https://mungingdata.com/python/jupyter-workflow-poetry-pandas/, accessed: 2020-10-28