Academic Research

Harness Enfathom’s comprehensive, news corpus—sentiment, entities, and narratives—to study historical trends, information diffusion, and socio‑economic dynamics at scale.

Common Problems

Researchers face several data hurdles when working with large‑scale media corpora:

Sampling Bias: Publicly available datasets often lack geographic or linguistic diversity, skewing results.
Manual Coding Overhead: Hand‑labeling sentiment or entities across thousands of articles is slow and error‑prone.
Historical Gaps: Many APIs offer only recent coverage, limiting longitudinal analyses.

Enfathom's Solution

Enfathom delivers a rich, historical news dataset through a simple REST API:

Historical Article Retrieval: Query /v1/articles with parameters like ?themes=climate%20policy&date=2000-01-01&to=2025-01-01 to retrieve 10+ years of multilingual coverage for longitudinal studies.
Sentiment & Polarity Scores: Each /v1/articles record contains sentence‑level sentiment (article.tone.net) so researchers skip manual sentiment coding.
Narrative Tracking: Use /v1/articles to explore how themes evolve over time—for example ?themes=renewable_energy to chart narrative momentum.

See detailed examples in our API Documentation.

Challenge

Academics need a scalable, unbiased corpus to test hypotheses across decades and languages—without the burden of manual data cleaning.

Solution

Enfathom provides a historical, multilingual news API with built‑in sentiment accelerating rigorous, reproducible academic research.