By requesting access, you agree: (1) research/education use only; (2) no redistribution/rehosting; (3) no commercial use; (4) no republishing substantial portions; (5) respect publishers’ rights and applicable laws; (6) access may be revoked for violations.

US Multi-Outlet News Headlines (2001–2024)

Dataset Summary

This dataset contains more than six million news headlines collected from five major U.S. news outlets between January 2001 and December 2024.

It was constructed to support longitudinal analysis of topic dynamics in U.S. news coverage across four presidential administrations (Bush, Obama, Trump, Biden). The dataset enables the study of structural shifts in topic attention, cross-outlet alignment, and synchronized responses to major political and social events.

This dataset is associated with the study:

“Long-Term Topic Dynamics in U.S. News Coverage” (2026).

Included outlets:

The New York Times (NYT)
The Los Angeles Times (LAT)
The Wall Street Journal (WSJ)
Fox News
MSNBC

Access and Usage

Due to copyright restrictions on publisher content, this dataset is distributed via gated access (request-based approval).

The dataset is provided for non-commercial academic research purposes only.

By requesting access, users agree:

Not to redistribute the headline text
To use the dataset solely for non-commercial academic research
To cite the associated publication when using the data

Dataset Structure

The repository contains:

Raw dataset (headline text and metadata as collected)
Preprocessed dataset (cleaned headline text and normalized dates)
LLM-category dataset (topic and category assignments used in the analysis)

Data Collection

Headlines were collected from official outlet sources via:

Archive APIs (e.g., The New York Times)
Public archive and sitemap pages

All parsing and preprocessing were conducted using standard HTML parsing and text-cleaning procedures.

Limitations

Archive availability differs across outlets.
Headlines are short textual summaries and may not fully reflect article-level framing.

Citation

If you find this dataset useful, please cite our paper:

@inproceedings{10.1145/3795766.3799771,
author = {Shim, Jaein and Kreutner, Maximilian and Strohmaier, Markus},
title = {Long-Term Topic Dynamics in U.S. News Coverage},
year = {2026},
isbn = {9798400725043},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3795766.3799771},
doi = {10.1145/3795766.3799771},
abstract = {With the rise of short-form content on social media, news headlines play an increasingly critical role in the perception of events. While prior studies have examined headlines related to specific short-term events, long-term trends remain largely unexplored. We present a dataset and analysis of more than six million headlines from five major U.S. outlets spanning four presidential administrations (2001–2024). Our analysis of political, social, and economic headlines reveals two primary trends. First, we observe a gradual shift toward greater alignment in core topic attention over time, particularly in domestic politics, alongside persistent differences in baseline editorial emphases across outlets. Second, major events such as the Russia-Ukraine War and COVID-19 pandemic trigger strong and synchronized shifts in topic salience across outlets. These findings point to a media environment characterized by growing overlap in core political topics and increasingly synchronized responses to major crises, while maintaining recognizable editorial distinctions across outlets. To support reproducibility and further longitudinal analysis, we will make our dataset available to researchers for non-commercial academic use.},
booktitle = {Proceedings of the 18th ACM Web Science Conference 2026},
pages = {409–420},
numpages = {12},
keywords = {Topic modeling, BERTopic, News headlines, Large Language Models, Media analysis},
location = {
},
series = {WebSci '26}
}

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