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Datasets:
lukeslp
/
us-attention-data

Tasks:
Time Series Forecasting
Feature Extraction
Languages:
English
Size:
1K<n<10K
Tags:
attention-metrics
wikipedia
gdelt
google-trends
united-states
media-analysis
License:
Dataset card Files
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metadata 
license: mit
task_categories:
  - time-series-forecasting
  - text-classification
language:
  - en
tags:
  - attention-metrics
  - wikipedia
  - google-trends
  - gdelt
  - pageviews
  - search-interest
  - media-coverage
  - sentiment-analysis
  - event-tracking
pretty_name: US Attention Data
size_categories:
  - n<1K

US Attention Data

Digital attention metrics tracking Wikipedia pageviews, Google Trends search interest, and GDELT global event coverage for United States-related topics throughout 2025.

Dataset Description

This dataset aggregates attention signals from three authoritative sources to track how digital attention flows around US-related topics:

  • Wikipedia Pageviews (Wikimedia REST API) - 2.5MB tracking 8 key articles across countries
  • Google Trends (pytrends API) - 810KB covering 138 search terms in the US region
  • GDELT 2.1 (Doc API) - Event mentions across 44 countries

Use Cases

  • Media attention analysis
  • Event impact measurement
  • Search trend correlation
  • Cross-platform attention patterns
  • News cycle dynamics
  • Public interest tracking

Dataset Structure

Data Files (10 total, 4.3 MB)

Wikipedia Pageviews

  • wikipedia_pageviews.json - 2.5 MB - Country-level pageview analysis
  • wikipedia_trending.json - 250 KB - Top 500 trending articles
  • wikipedia_event_articles.json - 210 KB - Event-driven article tracking

Google Trends

  • trends_data.json - 810 KB - 138 search terms, full year time series
  • weekly_trends.json - 26 KB - Weekly aggregated snapshot

GDELT Event Coverage

  • gdelt_timeline.json - 131 KB - 44 countries, event mentions
  • gdelt_weekly_events.json - 158 KB - Weekly event aggregation

Unified Cross-Source

  • events_unified.json - 88 KB - 177 events combined
  • unified_data.json - 27 KB - High-level unified analysis
  • weekly_attention_timeline.json - 56 KB - Weekly cross-platform metrics

Metadata Files

Each data file has a companion *_metadata.json with:

  • Source API details
  • Field descriptions
  • Update timestamps
  • Record counts
  • Usage notes

Data Collection

Wikipedia (Wikimedia REST API)

  • Endpoint: https://wikimedia.org/api/rest_v1
  • Auth: None required
  • Rate Limit: 5000 req/hour per IP
  • License: CC0 Public Domain

Google Trends (pytrends)

  • Package: pytrends Python library
  • Auth: None required
  • Rate Limit: Soft limits (use responsibly)
  • License: Google Terms of Service

GDELT (GDELT 2.1 Doc API)

  • Endpoint: https://api.gdeltproject.org/api/v2/doc/doc
  • Query: ("United States" OR "United States of America")
  • Auth: None required
  • License: Free for research and commercial use

Loading the Data 

import json
import pandas as pd

# Load Wikipedia pageviews
with open('data/wikipedia_pageviews.json') as f:
    wp_data = json.load(f)

# Load Google Trends
trends = pd.read_json('data/trends_data.json')

# Load GDELT events
with open('data/gdelt_timeline.json') as f:
    gdelt = json.load(f)

Key Findings

  • Wikipedia serves as primary information source with event-driven pageview spikes
  • Google Trends shows search volatility averaging 3-5 day persistence
  • GDELT reveals uneven global media coverage (Western bias)
  • Cross-source correlation during major events (disasters, elections, conflicts)
  • Attention decay follows exponential curve with 2-4 day half-life

Visualizations

Three live dashboards built on this data:

  1. One-Year US Global Sentiment - dr.eamer.dev/datavis/one-year

    • Uses: gdelt_timeline.json
    • Visualizes global media coverage patterns

  2. Trends 2025 - dr.eamer.dev/datavis/trends-2025

    • Uses: trends_data.json
    • Interactive Google Trends dashboard

  3. Wikipedia Attention Analytics - dr.eamer.dev/datavis/wiki-attention

    • Uses: wikipedia_pageviews.json
    • Pageview trend analysis

Citation 

@dataset{steuber2026attention,
  title={US Attention Data: Digital Attention Metrics from Wikipedia, Google Trends, and GDELT},
  author={Steuber, Luke},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/lukeslp/us-attention-data}
}

License

MIT License - See LICENSE file for details.

Data sources have their own licenses:

  • Wikipedia data: CC0 Public Domain
  • Google Trends: Subject to Google Terms of Service
  • GDELT: Free for research and commercial use

About

Created by Luke Steuber