Building a Pipeline to Study War Coverage Using GDELT

2026-04-20 · Source: HackerNoon · Field: Technology & Digital — Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article details a Python-based methodology for scraping, cleaning, classifying, and visualizing 113 news headlines to analyze media framing after the April 8, 2026, Israeli strikes on Beirut. The author utilized the GDELT Project's Document 2.0 API for data collection, focusing on headline-level metadata from international sources. The tech stack was intentionally minimal, relying on Python 3.14, `requests` for API communication, and `matplotlib` for visualization, avoiding heavy libraries like pandas or NLP frameworks. The collection pipeline employed a recursive windowing strategy to handle GDELT's 250-article response cap and implemented exponential backoff for rate limiting. Data cleaning involved keyword, geographic, and thematic filters, reducing 528 raw articles to 113. Manual classification was performed for political/military versus human impact framing, and a regional analysis grouped articles by source country.

Key takeaway

For data scientists or analysts building rapid media framing studies, you should prioritize minimal dependencies and robust API handling, including exponential backoff, from the outset. For datasets under 300-500 items, your classification will likely be faster and more accurate if done manually rather than attempting to build and validate an automated classifier, ensuring higher data integrity for your analysis.

Key insights

Manual classification can be more efficient and accurate than automation for small, unambiguous datasets.

Principles

• Minimize dependencies for reproducibility.
• Design queries to avoid API parsing issues.
• Build backoff into API requests early.

Method

Scrape GDELT headlines using recursive windowing and exponential backoff, clean data via keyword, geographic, and thematic filters, then manually classify and visualize using Matplotlib.

In practice

• Use GDELT for rapid, broad headline collection.
• Implement URL and normalized title deduplication.
• Consider manual classification for <300-500 items.

Topics

• GDELT API
• Media Framing
• Data Pipeline
• Python Programming
• Manual Classification

Best for: Data Scientist, Software Engineer, Research Scientist

Related on AIssential

• Are CAQDAS tools appropriate for large datasets? — Modern CAQDAS tools vary widely in scalability and performance when analyzing large qualitative datasets.
• Methodology — This methodology combines large-scale social media analysis with national survey data to study health influencers and audience engagement.
• I spent 4 days training an AI model to understand my country. It barely works — Low-resource NLP projects face significant challenges in data collection, translation, and accurate labeling, often yielding modest baselines.
• Andrej Karpathy Dropped a 200-Line GPT; I Used the Same Math To Turn Datasets Into Searchable… — Automating statistical pipelines can eliminate manual data entry and streamline research execution.
• How Andrej Karpathy Built a Working Transformer in 243 Lines of Code — microGPT offers a transparent, dependency-free Python implementation of GPT-2 fundamentals for educational purposes.
• Making Sense of the Early Universe — AI and GPU acceleration dramatically speed up astronomical data analysis, enabling discoveries of distant galaxies.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.