I recently dug into Wikipedia’s data on Philippine corruption using some Python magic, and the patterns that emerged were really fascinating. Let me walk you through what I discovered.

After analyzing 51 documented corruption cases, I found:

11 different types of corruption that keep popping up

1. Kickbacks take the top spot (15.7% of cases)
2. Bribery comes in second (13.7%)
3. Election fraud and overpricing tie for third (11.8% each)

What really surprised me was how often these cases involve multiple corruption types. The PDAF scam, for instance, showed up under nine different categories!

The Corruption Network 🕸️

When I mapped everything out, it looked like a spiderweb of connections:

https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6jipj2pn9ougmvj2s35d.png

Each case (blue nodes) connects to the corruption types it involved (red nodes). The bigger the red node, the more cases fell into that category. What stood out? How frequently "election fraud" and "nepotism" appear together with financial crimes.

A Historical Pattern Emerges

Looking across different political eras revealed some interesting trends:

https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0gser5psiqj4blqvqznt.png

• Marcos Era: The classic "plunder + embezzlement" combo
• Post-EDSA: Things quieted down (at least in documentation)
• Arroyo Era: Election fraud takes center stage
• PDAF Era (2013): Pork barrel abuse becomes systematic
• Recent Years: Healthcare and procurement issues surface It’s like each era developed its own "corruption signature."

Behind the Scenes 🔧

Here’s how I approached this:

• Data Collection: Used wikipedia-api to pull content from corruption-related pages
• Automated Categorization: Created a keyword system that automatically tagged cases (looking for terms like "ghost projects," "lagay," "overpricing")
• Visualization: Used networkx to show connections and seaborn for the historical heatmap
• Manual Enhancement: Added some well-known cases that Wikipedia might not have categorized fully The cool part? This whole analysis runs automatically. You can point it at different Wikipedia corruption pages and see similar patterns emerge.

My Takeaway

What struck me most wasn’t just the volume of cases, but how interconnected different corruption types are. Financial crimes rarely happen in isolation - they often involve election manipulation, nepotism, and contract rigging working together.

The data also shows how corruption evolves. Each political generation seems to develop new methods while keeping old favorites like bribery and kickbacks in the toolkit.