dnakhla/us-news-headlines-enriched

US News Headlines Enriched

A longitudinal, enriched dataset of 143,142 US news headlines spanning 2015 to 2026 from 13 major outlets. Every headline is enriched with NER, topic clusters, sentiment scores, semantic anchor distances, and Vextant media framing scores. Pre-computed text-embedding-3-small embeddings (1536-dim) are included as a separate file.

Dataset Summary

Stat	Value
Total headlines	143,142
Current era (2025-2026)	118,113
Historical era (2015-2016)	25,029
Outlets	13
Date range	2015-01-01 to 2026-03-22
Enrichment layers	6 (NER, topics, sentiment, anchors, Vextant, embeddings)
Embedding dimensions	1,536 (text-embedding-3-small)

Outlets

Outlet	Count
NYT	49,212
Guardian	30,169
Al Jazeera	8,891
Reuters	8,223
NY Post	6,908
MSNBC	6,728
AP	6,290
Fox News	5,951
Washington Post	5,630
Breitbart	4,136
BBC	3,976
WSJ	3,526
CNN	3,502

Era Column

The era column distinguishes the two collection periods:

• current (118,113 records): Headlines from January 2025 to March 2026, collected via official APIs (NYT Archive, Guardian Open Platform, etc.)
• historical (25,029 records): Headlines from 2015-2016, collected via Wayback Machine CDX and GDELT GKG bulk files

This enables longitudinal framing analysis across a ~10-year span of US news coverage.

Enrichment Layers

1. Named Entity Recognition (NER)

• ner_persons, ner_orgs, ner_locations: Extracted using spaCy NER pipeline
• Enables entity-level analysis of coverage patterns

2. Topic Clusters

• topic_cluster (int): K-means cluster ID from embedding space (k=40)
• topic_label (string): Human-readable cluster label (e.g., "trump_shutdown_bill", "2016_election_campaign")
• Clusters computed separately per era to capture era-specific topic structure

3. Sentiment Analysis

• sentiment_label: positive / negative / neutral
• sentiment_positive, sentiment_negative, sentiment_neutral: Class probabilities
• sentiment_score: Compound score (-1 to +1)
• Model: cardiffnlp/twitter-roberta-base-sentiment-latest

4. Semantic Anchor Distances

Cosine similarity scores measuring proximity of each headline's embedding to 26 semantic anchor concepts:

Anchor pair	Fields
Conservative / Progressive	anchor_conservative, anchor_progressive
Patriotic / Anti-establishment	anchor_patriotic, anchor_anti_establishment
Conflict / Peace	anchor_conflict, anchor_peace
Threat / Safety	anchor_threat, anchor_safety
Prosperity / Hardship	anchor_prosperity, anchor_hardship
Free Market / Govt Intervention	anchor_free_market, anchor_government_intervention
Justice / Law and Order	anchor_justice, anchor_law_and_order
Immigration Welcome / Restrict	anchor_immigration_welcome, anchor_immigration_restrict
Institutional Trust / Populist Skepticism	anchor_institutional_trust, anchor_populist_skepticism
Measured / Sensational	anchor_measured, anchor_sensational
Domestic Focus / Global Focus	anchor_domestic_focus, anchor_global_focus
Tech Optimism / Tech Concern	anchor_tech_optimism, anchor_tech_concern
Climate Urgency	anchor_climate_urgency

5. Vextant Media Framing Scores

Semantic axis projection scores from the Vextant AlignmentEngine, measuring four dimensions of media framing:

• vextant_conflict_intensity: Conflict-laden vs. neutral/cooperative framing
• vextant_agency_assignment: Active agency vs. passive/structural framing
• vextant_moralized_language: Morally charged vs. neutral language
• vextant_institutional_stance: Institutional trust vs. skepticism framing

6. Embeddings

Pre-computed embeddings using OpenAI text-embedding-3-small (1536 dimensions) are stored in embeddings_combined.npz. The embedding_index field maps each headline to its row in the embedding matrix.

import numpy as np
from datasets import load_dataset

ds = load_dataset("dnakhla/us-news-headlines-enriched", split="train")
from huggingface_hub import hf_hub_download
emb_path = hf_hub_download("dnakhla/us-news-headlines-enriched", "embeddings_combined.npz", repo_type="dataset")
embeddings = np.load(emb_path)["embeddings"]

Metadata Fields

Fields available primarily for current era records (may be null for historical):

• section: News section (e.g., "U.S.", "World", "Business")
• author: Article byline
• description: Article summary/description
• word_count: Article word count
• topic: Original topic classification
• religion_tags, geographic_entities, named_entities: Additional extracted entities
• headline_type: Declarative, interrogative, imperative, etc.

Usage

from datasets import load_dataset

# Load full dataset
ds = load_dataset("dnakhla/us-news-headlines-enriched", split="train")

# Filter by era
current = ds.filter(lambda x: x["era"] == "current")
historical = ds.filter(lambda x: x["era"] == "historical")

# Filter by outlet
nyt = ds.filter(lambda x: x["outlet"] == "nyt")

# Get high-conflict headlines
high_conflict = ds.filter(lambda x: x["vextant_conflict_intensity"] is not None and x["vextant_conflict_intensity"] > 0.5)

# Compare sentiment across outlets
import pandas as pd
df = ds.to_pandas()
df.groupby("outlet")["sentiment_score"].mean().sort_values()

Use Cases

• Cross-outlet framing analysis: Compare how different outlets frame the same events
• Longitudinal media studies: Track framing shifts from 2015 to 2026
• Sentiment analysis benchmarking: Pre-computed sentiment with source probabilities
• Topic modeling research: Pre-clustered topics with embeddings for custom clustering
• NER evaluation: Entity extraction across diverse news sources
• Embedding-based retrieval: Pre-computed embeddings for semantic search and similarity

Collection Methodology

• Current era: Official news APIs (NYT Archive API, Guardian Open Platform, NewsAPI) with stratified sampling across outlets
• Historical era: Wayback Machine CDX and GDELT GKG bulk files for 2015-2016 headline recovery
• Filtering: Op-eds excluded; news articles only (article_type: news)
• Enrichment: Automated pipeline — spaCy NER, k-means topic clustering on embeddings, cardiffnlp sentiment, cosine anchor distances, Vextant AlignmentEngine scoring

Citation

@dataset{nakhla2026usheadlines,
  title={US News Headlines Enriched: A Longitudinal Multi-Outlet Corpus with Framing Annotations},
  author={Nakhla, Danny},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/dnakhla/us-news-headlines-enriched}
}

License

CC-BY-4.0