GAIN-H: Global AI News Headlines Corpus with Multilingual and Temporal Features

Global news sources provide over 2.5 million artificial intelligence-related headlines across multiple languages and countries. The collection includes three complementary datasets designed for temporal, linguistic, and keyword-stratified analysis, spanning more than two decades of coverage. It was developed as part of the RAISE initiative at Rutgers University's MPI Program, Bloustein School.

Use Cases

• Analyzing media framing and sentiment of AI topics based on the metadata-rich corpus with temporal and linguistic features.
• Conducting longitudinal studies of AI discourse trends based on the large-scale temporal analysis corpus.
• Performing cross-lingual or country-specific comparisons of AI news coverage based on the multilingual corpus.
• Studying AI governance and communication patterns based on keyword-stratified metadata.

Strengths

• Contains over 2.5 million AI-related news headlines.
• Spans more than two decades of temporal coverage for longitudinal analysis.
• Includes three specialized datasets for metadata-rich, temporal, and multilingual analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for individual datasets is unknown, which may limit suitability assessment.
• Data may reflect geographic, linguistic, or source bias inherent to the selected news sources.

Provenance

Source

RAISE (Rethinking AI for Shared Empowerment) initiative at the MPI Program, Bloustein School, Rutgers University, and AIXosphere.

Collection Method

Gathered from global news sources across multiple languages and countries.

Time Range

Spans more than two decades (specific start year not provided).

Freshness

Last updated 2026-06-06 00:25:28; freshness should be verified.

Geography

Global coverage across multiple countries.