Datasets:

LeFrenchNewsLab
/

frenchnews-7

pretty_name: FrenchNews-7
language:
  - fr
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/frenchnews7_manifest.csv
tags:
  - french
  - news
  - journalism
  - text-classification
  - computational-journalism
  - topic-classification
  - editorial-desk-classification

FrenchNews-7

FrenchNews-7 is a cross-publisher French news editorial desk classification benchmark. This public release is a manifest-only artifact for reproducibility and benchmarking: it exposes URL-level and metadata-level information for 87,637 labeled articles from 13 publishers without redistributing article text.

The target variable is the publisher's editorial routing decision — which desk a newsroom assigned an article to — rather than an annotator's perceived topic. The label spaces overlap heavily with conventional news topics, but the label-generating process is what gives the resource its purpose: recovering routing decisions at scale is what enables agenda-setting and media-diversity research.

What This Release Contains

This repository includes:

A manifest CSV with article identifiers, source URLs, publishers, labels, split assignments, dates, and years
Taxonomy files describing the seven-label classification scheme
Dataset statistics and publisher metadata
Lightweight reconstruction utilities for users who are legally authorized to refetch source pages themselves

This repository does not include:

Article body text
Headlines
Snippets
Summaries
Embeddings
Any other verbatim textual content from the source publishers

Intended Use

FrenchNews-7 is intended for research and benchmarking in French news editorial-desk classification, especially cross-publisher evaluation under a harmonized 7-label taxonomy. It is not intended as a universal topic ontology for all French text.

Recommended Model

The recommended deployment artifact for this dataset is the headline + body CamemBERT-base classifier released here:

LeFrenchNewsLab/camembert-base-frenchnews7

Dataset Summary

Dataset name: FrenchNews-7
Language: French
Task: 7-way editorial desk classification
Articles: 87,637
Publishers: 13
Time span: 2005-04-14 to 2026-03-12
Public release format: manifest-only

Splits

Split	Articles
train	61,345
validation	13,146
test	13,146

Stratified, seed = 42.

Note on the dataset viewer. The manifest ships as a single CSV; the split column carries the partition. The Hub viewer therefore shows all 87,637 rows under one train view. Filter on the split column to recover the three partitions:
from datasets import load_dataset
ds = load_dataset("LeFrenchNewsLab/frenchnews-7", split="train")
test = ds.filter(lambda r: r["split"] == "test")

Label Distribution

Label	Articles
Société	19,324
International	18,642
Culture & Loisirs	18,350
Politique	10,546
Sport	8,404
Économie	8,174
Sciences & Technologies	4,197

Annotation

Labels are assigned by a two-bucket pipeline that prefers deterministic assignment over model judgment wherever possible.

Bucket	Method	Articles	Share
A	Deterministic slug rules (74 rules)	63,302	72.2%
B	LLM zero-shot (temperature = 0)	24,335	27.8%

Bucket A matches the first URL path segment against a 74-rule lookup table (annotation/bucket_a_slug_rules.json). Matching is case-insensitive — several publishers emit capitalised segments such as /Culture/, /Societe/ and /International/. Reproducing with case-sensitive matching yields 58,926 instead of 63,302.

Bucket B covers articles whose slug carries no unambiguous desk signal (/idees, /debats, /story, /flash-actu, regional or absent slugs).

Annotation quality

A four-rater agreement study was run on a 300-article stratified random sample of Bucket B (GPT-OSS-120B as original labeller, Gemini-2.5-flash-lite as independent second LLM, an adjudicator, and a blinded second human annotator shown headline and body only — no URLs, outlet names, or model labels).

Rater pair	Agreement	Cohen's κ
Blinded human ↔ reference labels	84.0%	0.806
GPT-OSS ↔ blinded human	80.7%	0.766
Original LLM ↔ Gemini	87.3%	0.846
Original LLM ↔ adjudicated gold	95.3%	0.943
Gemini ↔ adjudicated gold	91.3%	0.895

Fleiss' κ = 0.835 across all four raters; three-rater Krippendorff's α = 0.789.

Bucket B labels are functionally inert for reported results: training on Bucket A only shifts held-out macro-F1 by −0.004 (95% CI [−0.024, +0.024]; McNemar p = 0.63).

Publishers

20 Minutes
JDD
L'Express
L'Humanité
La Croix
Le Figaro
Le HuffPost
Le Monde
Le Parisien
Le Point
Ouest-France
Slate.fr
TF1 INFO

Manifest Schema

Main manifest file:

data/frenchnews7_manifest.csv

Required columns:

id
url
publisher
label
split
date
year

No text-bearing columns are included. In particular, the release excludes headline, body, snippet, and summary fields.

Example rows:

id,url,publisher,label,split,date,year
fn7-1089,https://www.lemonde.fr/economie/article/2025/06/23/...,Le Monde,Économie,validation,2025-06-23,2025
fn7-2969,https://www.lemonde.fr/idees/article/2025/06/13/...,Le Monde,Sciences & Technologies,test,2025-06-13,2025

Labels reflect the harmonized benchmark taxonomy and may not correspond literally to the URL path slug in all cases.

Label Taxonomy

Société: domestic social affairs, human interest, crime, health, education, and civil society
Culture & Loisirs: arts, cinema, music, television, books, leisure, and lifestyle
International: foreign affairs, geopolitics, and global events outside France
Politique: domestic politics, government, elections, institutions, and legislative affairs
Sport: all sports coverage regardless of discipline
Économie: business, finance, macroeconomics, markets, and corporate news
Sciences & Technologies: scientific research, technology, digital innovation, and environment-science topics

Limitations

The release is manifest-only and does not redistribute article text.
Labels reflect a harmonized cross-publisher editorial taxonomy rather than arbitrary fine-grained semantic annotation.
Reconstruction success may vary over time as publisher pages change or disappear.
Performance and coverage may vary across outlets, years, and topic distributions.
Bucket A slug coverage varies by publisher, from 100% (JDD) to 34.3% (Slate.fr).

Legal Note

This repository is designed to support reproducibility without redistributing copyrighted news content. Users are responsible for ensuring that any downstream fetching, storage, processing, or redistribution of source material complies with applicable law, publisher terms, robots directives, and institutional policy.

The CC-BY-4.0 license applies only to the original manifest structure, labels, taxonomy definitions, and repository-authored materials released by the authors, and does not apply to the linked publisher content.

Reconstruction Note

The reconstruction/ folder provides lightweight starter utilities to refetch pages from the original URLs under the user's own legal and operational framework. These utilities are intentionally conservative and do not attempt aggressive scraping or text extraction. They are provided solely as a convenience for authorized users.

Citation

If you use FrenchNews-7 or the accompanying classifier, please cite the associated paper.

@misc{sobhy2026frenchnews7,
  title        = {FrenchNews-7: Benchmarking Cross-Publisher French News Editorial Desk Classification},
  author       = {Amr Sobhy},
  year         = {2026},
  note         = {Working paper, version April 2026},
}

Project page: https://frenchnewslab.org/en/publications/frenchnews-7-benchmarking-cross-publisher-french-news-topic-classification