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gdelt-gkg-march2020-v2

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news
global
knowledge-graph
geopolitics
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metadata
license: cc-by-4.0
tags:
  - text
  - news
  - global
  - knowledge-graph
  - geopolitics
dataset_info:
  features:
    - name: GKGRECORDID
      dtype: string
    - name: DATE
      dtype: string
    - name: SourceCollectionIdentifier
      dtype: string
    - name: SourceCommonName
      dtype: string
    - name: DocumentIdentifier
      dtype: string
    - name: V1Counts
      dtype: string
    - name: V2.1Counts
      dtype: string
    - name: V1Themes
      dtype: string
    - name: V2EnhancedThemes
      dtype: string
    - name: V1Locations
      dtype: string
    - name: V2EnhancedLocations
      dtype: string
    - name: V1Persons
      dtype: string
    - name: V2EnhancedPersons
      dtype: string
    - name: V1Organizations
      dtype: string
    - name: V2EnhancedOrganizations
      dtype: string
    - name: V1.5Tone
      dtype: string
    - name: V2GCAM
      dtype: string
    - name: V2.1EnhancedDates
      dtype: string
    - name: V2.1Quotations
      dtype: string
    - name: V2.1AllNames
      dtype: string
    - name: V2.1Amounts
      dtype: string
    - name: tone
      dtype: float64
  splits:
    - name: train
      num_bytes: 3331097194
      num_examples: 281215
    - name: negative_tone
      num_bytes: 3331097194
      num_examples: 281215
  download_size: 2229048020
  dataset_size: 6662194388
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: negative_tone
        path: data/negative_tone-*

Dataset Card for dwb2023/gdelt-gkg-march2020-v2

Dataset Details

Dataset Description

This dataset contains GDELT Global Knowledge Graph (GKG) data covering March 10-22, 2020, during the early phase of the COVID-19 pandemic. It captures global event interactions, actor relationships, and contextual narratives to support temporal, spatial, and thematic analysis.

  • Curated by: dwb2023

Dataset Sources

Uses

Direct Use

This dataset is suitable for:

  • Temporal analysis of global events
  • Relationship mapping of key actors in supply chain and logistics
  • Sentiment and thematic analysis of COVID-19 pandemic narratives

Out-of-Scope Use

  • Not designed for real-time monitoring due to its historic and static nature
  • Not intended for medical diagnosis or predictive health modeling

Dataset Structure

Features and Relationships

  • this dataset focuses on a subset of features from the source GDELT dataset.
Name Type Aspect Description
DATE string Metadata Publication date of the article/document
SourceCollectionIdentifier string Metadata Unique identifier for the source collection
SourceCommonName string Metadata Common/display name of the source
DocumentIdentifier string Metadata Unique URL/identifier of the document
V1Counts string Metrics Original count mentions of numeric values
V2.1Counts string Metrics Enhanced numeric pattern extraction
V1Themes string Classification Original thematic categorization
V2EnhancedThemes string Classification Expanded theme taxonomy and classification
V1Locations string Entities Original geographic mentions
V2EnhancedLocations string Entities Enhanced location extraction with coordinates
V1Persons string Entities Original person name mentions
V2EnhancedPersons string Entities Enhanced person name extraction
V1Organizations string Entities Original organization mentions
V2EnhancedOrganizations string Entities Enhanced organization name extraction
V1.5Tone string Sentiment Original emotional tone scoring
V2GCAM string Sentiment Global Content Analysis Measures
V2.1EnhancedDates string Temporal Temporal reference extraction
V2.1Quotations string Content Direct quote extraction
V2.1AllNames string Entities Comprehensive named entity extraction
V2.1Amounts string Metrics Quantity and measurement extraction

Aspects Overview:

  • Metadata: Core document information
  • Metrics: Numerical measurements and counts
  • Classification: Categorical and thematic analysis
  • Entities: Named entity recognition (locations, persons, organizations)
  • Sentiment: Emotional and tone analysis
  • Temporal: Time-related information
  • Content: Direct content extraction

Dataset Creation

Curation Rationale

This dataset was curated to capture the rapidly evolving global narrative during the early phase of the COVID-19 pandemic, focusing specifically on March 10–22, 2020. By zeroing in on this critical period, it offers a granular perspective on how geopolitical events, actor relationships, and thematic discussions shifted amid the escalating pandemic. The enhanced GKG features further enable advanced entity, sentiment, and thematic analysis, making it a valuable resource for studying the socio-political and economic impacts of COVID-19 during a pivotal point in global history.

Curation Approach

A targeted subset of GDELT’s columns was selected to streamline analysis on key entities (locations, persons, organizations), thematic tags, and sentiment scores—core components of many knowledge-graph and text analytics workflows. This approach balances comprehensive coverage with manageable data size and performance. The ETL pipeline used to produce these transformations is documented here: https://gist.github.com/donbr/e2af2bbe441f90b8664539a25957a6c0.

Citation

When using this dataset, please cite both the dataset and original GDELT project:

@misc{gdelt-gkg-march2020,
    title = {GDELT Global Knowledge Graph March 2020 Dataset},
    author = {dwb2023},
    year = {2025},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/dwb2023/gdelt-gkg-march2020-v2}
}

Dataset Card Contact

For questions and comments about this dataset card, please contact dwb2023 through the Hugging Face platform.