--- 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 - **Repository:** [http://data.gdeltproject.org/gdeltv2](http://data.gdeltproject.org/gdeltv2) - **GKG Documentation:** [GDELT 2.0 Overview](https://blog.gdeltproject.org/gdelt-2-0-our-global-world-in-realtime/), [GDELT GKG Codebook](http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook-V2.1.pdf) ## 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](https://gist.github.com/donbr/e2af2bbe441f90b8664539a25957a6c0). ## Citation When using this dataset, please cite both the dataset and original GDELT project: ```bibtex @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.