metadata
license: mit
task_categories:
- time-series-forecasting
- feature-extraction
language:
- en
tags:
- attention-metrics
- wikipedia
- gdelt
- google-trends
- united-states
- media-analysis
- time-series
pretty_name: US Attention Data
size_categories:
- 1K<n<10K
US Attention Data
Weekly cross-platform attention metrics for tracking how much the world pays attention to the United States. Combines Wikipedia pageviews, GDELT global event mentions, and Google Trends search interest from 2020-2025.
I built this dataset for the one-year visualization project, which maps US global sentiment over time. Part of the Data Trove collection.
What's Inside
| File | Size | Description |
|---|---|---|
wikipedia_pageviews.json |
2.5 MB | Daily pageview counts for US-related Wikipedia articles |
wikipedia_event_articles.json |
214 KB | Event-linked article metadata |
wikipedia_trending.json |
256 KB | Trending article detection |
trends_data.json |
810 KB | Google Trends search interest over time |
weekly_trends.json |
26 KB | Weekly trending topic aggregations |
gdelt_timeline.json |
131 KB | GDELT event mention timelines |
gdelt_weekly_events.json |
158 KB | GDELT weekly aggregated event counts and tone |
events_unified.json |
89 KB | Unified event data across all sources |
weekly_attention_timeline.json |
57 KB | Combined weekly attention metrics |
unified_data.json |
27 KB | Merged dataset across all attention sources |
attention_metadata.json |
2 KB | Collection metadata and schema |
Total: ~4.2 MB
Quick Start
Python
import json
with open("wikipedia_pageviews.json") as f:
pageviews = json.load(f)
# Weekly attention across all sources
with open("weekly_attention_timeline.json") as f:
timeline = json.load(f)
D3.js
const pageviews = await d3.json("wikipedia_pageviews.json");
const gdelt = await d3.json("gdelt_weekly_events.json");
Data Sources
| Source | What It Tracks | Coverage |
|---|---|---|
| Wikipedia Pageviews API | Article view counts | 2020-2025, daily |
| GDELT Project | Global event mentions and media tone | 2020-2025, weekly |
| Google Trends | Search interest indices | 2020-2025, weekly |
Use Cases
- Tracking how global attention to the US shifts over time
- Correlating media events with Wikipedia traffic and search interest
- Identifying seasonal attention patterns (elections, holidays, crises)
- Building composite attention indices from multiple independent signals
Related
- one-year visualization -- the viz this data powers
- Data Trove -- full dataset catalog
- lukesteuber.com -- portfolio
Author
Luke Steuber -- @lukesteuber.com on Bluesky
License
MIT. See LICENSE.
Data sourced from Wikipedia (CC BY-SA), GDELT (open), and Google Trends (fair use for research).