SinclairSchneider/rtl_de

RTL News (rtl.de)

187 425 German-language news articles crawled from www.rtl.de and published as a Hugging Face dataset. This is part of the German Media collection, a set of comparable corpora covering the German media landscape from public-service broadcasters to alternative and fringe outlets, all built with the same crawler and the same extraction pipeline so that they can be compared and combined directly.

⚠️ For scientific research only

This corpus is published exclusively for scientific research under the German research exception for text and data mining — the Forschungsprivileg of § 60d UrhG (implementing Art. 3 of EU Directive 2019/790). The article texts remain the copyright of their publisher and are not licensed for commercial use, redistribution, or republication. By downloading it you confirm that you use it for non-commercial scientific research. See Licensing and intended use.

The articles are reproduced verbatim from the publisher's website; their content expresses the views of that publisher, not of the dataset author.

At a glance

Source	www.rtl.de
Rows	187 425
Columns	9 (title, content, author, description, keywords, category, datePublished, dateModified, url)
Language	German (de)
Split	train (single split)
Format	Parquet, 2 shards, ≈ 311 MB download / 536 MB in memory
Perceived partisanship	4.5 / 7 → relatively neutral (medienkompass.org reader survey)
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/rtl_de

from datasets import load_dataset

ds = load_dataset("SinclairSchneider/rtl_de", split="train")
print(ds[0]["title"])

About the source

rtl.de is the online portal of RTL Deutschland, Germany's largest commercial broadcaster (Bertelsmann/RTL Group). The portal mixes news with a strong share of service, lifestyle, celebrity and television-related coverage, so this dataset is markedly less political in composition than the broadsheet and public-broadcasting datasets in the same collection.

Perceived political position

The German media compass at medienkompass.org places German mainstream and alternative outlets on two axes on the basis of a reader survey (n ≈ 1 154 participants in total, building on a media map by the think tank polisphere; the quality axis is adapted from Vanessa Otero's media chart).

• x axis — Parteilichkeit (partisanship), 1–7: < 3.5 left, 3.5 – 4.5 relatively neutral, > 4.5 right. 1 far-left (fake news and conspiracy theories) · 2 left mission (questionable journalistic standards) · 3 leaning left · 4 minimal partisan tendency · 5 leaning right · 6 right mission (questionable journalistic standards) · 7 far-right (fake news and conspiracy theories).
• y axis — Qualität (quality), 1–5: 1 sensationalist / clickbait · 2 basic information · 3 meets high standards · 4 analytical · 5 complex.

In that survey, RTL is rated:

	Score	Reading
Partisanship (1–7)	4.5	relatively neutral
Quality (1–5)	1.5	on the 1 = clickbait … 5 = complex scale

Parteilichkeit (partisanship):  1 = far left  ...  7 = far right

1         2         3         4         5         6         7
|---------|---------|---------|---------|---------|---------|
                                   ^                         
                                  RTL = 4.5

left  <--------------|  neutral  |-------------->  right
                   3.5           4.5

For context, here is the full survey table, sorted from left to right — the other outlets in the collection are directly comparable:

Outlet	Partisanship (1–7)	Quality (1–5)
indymedia	2.0	1.7
Jungle World	2.3	2.3
junge Welt	2.4	2.3
neues Deutschland	2.6	2.3
der Freitag	2.7	2.9
die Tageszeitung	2.8	2.7
Vice	2.8	1.8
NachDenkSeiten	3.1	3.0
Frankfurter Rundschau	3.4	2.8
SPON	3.5	2.1
Spiegel Print	3.5	2.9
Süddeutsche	3.5	3.3
WDR	3.5	2.8
Tagesspiegel	3.6	2.8
Zeit	3.6	3.5
ARD	3.7	2.9
NDR	3.7	2.8
Deutschlandfunk	3.8	3.5
Stern	3.8	2.1
ZDF	3.8	2.8
MDR	4.1	2.5
Handelsblatt	4.3	3.3
NTV	4.3	2.1
BR	4.4	2.6
N24	4.4	2.0
Wirtschaftswoche	4.4	3.1
Frankfurter Allgemeine	4.5	3.3
RTL ⬅ this dataset	4.5	1.5
Die Welt	4.8	2.5
Cicero	4.9	3.1
Focus Online	4.9	1.7
Deutsche Wirtschafts Nachrichten	5.0	2.2
MM News	5.1	1.7
RT Deutsch	5.1	2.0
Achgut	5.2	2.4
BILD	5.2	1.3
Tichys	5.5	2.6
junge Freiheit	5.8	1.9
Compact	6.0	1.8

How to read these numbers. They are perception data from a self-selected online survey, not a content analysis: participants rated outlets they know, the sample is not representative of the German population, and the site itself describes the project as an early iteration. Treat them as a rough, crowd-sourced orientation — useful for grouping datasets into coarse political blocks, not as ground truth for bias labelling.

Dataset structure

Single train split with 187 425 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	headline from the ld+json block containing headline
content	string	Full plain-text body of the article.	articleBody from the ld+json block containing articleBody
author	string	Author(s) as published by the outlet.	author from ld+json — when it is a list, the last entry's name is used
description	string	Teaser / meta description of the article.	description from ld+json
keywords	list[string]	Topic keywords / tags assigned by the publisher.	<meta name="keywords">, split on , and stripped
category	string	Section the article was published in.	a.Breadcrumb_link__VP_Re entries whose href has more than two path segments (reverse-sorted, first)
datePublished	string	Publication timestamp.	datePublished from ld+json
dateModified	string	Timestamp of the last editorial update.	dateModified from ld+json
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Scheinfasten: Leichter Abnehmen durch Fasten ohne Hungern",
  "content": "Ihr wollt schnell ein paar Pfunde loswerden, habt aber keine Lust auf Verzicht?\nDann könnte Scheinfasten genau das Richtige für euch sein. Durch die Wahl der richtigen Lebensmittel täuscht ihr eurem Körper einen Nahrungsmangel vor, sodass er in den Fasten-Modus schaltet. Wir verraten euch, wie euer Speiseplan dafür optimalerweise zusammengesetzt sein sollte.  Was unterscheidet das Scheinfasten vom …",
  "author": "Nora Rieder",
  "description": "Beim Scheinfasten täuscht ihr eurem Körper durch eine spezielle Lebensmittelzusammenstellung einen Nahrungsmangel vor.  Folglich schaltet er in den Fasten-Modus. Die Folge: Ihr baut Körperfett ab und die die Pfunde purzeln.   ",
  "keywords": [
    "Diät",
    "Abnehmen",
    "schnell abnehmen",
    "gesund abnehmen",
    "Abnehmen Tipps",
    "Diätplan",
    "Diät Tipps",
    "Ernährungsplan",
    "…"
  ],
  "category": "Abnehmen",
  "datePublished": "2025-05-20T16:13:03+02:00",
  "dateModified": "2025-05-20T16:13:03+02:00",
  "url": "https://www.rtl.de/ratgeber/gesundheit/abnehmen/scheinfasten-leichter-abnehmen-durch-fasten-ohne-hungern-id4370416.html"
}

Temporal coverage

The datePublished column could be parsed for 187 425 of 187 425 rows (100.0 %). Those rows span 2009-08-21 → 2025-06-14.

Year	Articles
2009	8	█
2010	30	█
2011	144	█
2012	468	█
2013	1 078	█
2014	2 482	██
2015	3 299	██
2016	7 900	█████
2017	2 957	██
2018	3 489	██
2019	6 971	█████
2020	13 913	██████████
2021	32 586	██████████████████████
2022	34 954	████████████████████████
2023	33 453	███████████████████████
2024	31 264	█████████████████████
2025	12 429	█████████

How the data was collected

The whole pipeline is contained in this repository under scraping/ and consists of two steps: a distributed crawler that walks the site and writes one JSON file per article, and a consolidation notebook that merges those files into the dataset you are reading.

1. Crawling — scraping/crawler.py

The crawler is a breadth-first whole-site crawler built on MechanicalSoup and multiprocessing:

1. Sharded frontier. 32 worker processes each own one multiprocessing.Manager().Queue(). A URL is assigned to a worker by int(md5(url).hexdigest(), 16) % 32, so every URL is always handled by the same worker and no locking or cross-process deduplication is needed.
2. Seed. The crawl is seeded with a single article URL (https://www.rtl.de/news/flugzeugabsturz-in-indien-studentin-verpasst-todesflieger-um-zehn-minuten-id4415181.html) and discovers everything else by following links.
3. Link discovery. Every fetched page is parsed with BeautifulSoup/lxml; all <a href> values that start with https://www.rtl.de/ or with / (which are then made absolute) are added to the frontier. URLs containing # are dropped.
4. URL filtering. Before fetching, a URL is skipped when it contains /files/, /video/, /api/, .jpg, .pdf, .xml, .pptx, .shtml or .epub, or when it has fewer than five path segments (which filters out section index pages).
5. Fetching. Each URL gets up to 10 attempts with a 10 s timeout.
6. Extraction. get_content() turns the HTML into a record (see below). Only pages with a non-empty content are written out, as pages/<flattened-url>.json.
7. Resumability. Each worker appends every known and every visited URL to data/thread_<id>_pages.txt and data/thread_<id>_pages_visited.txt, and re-reads them on start, so an interrupted crawl continues where it stopped. Creating a file named killall.txt in the working directory stops all workers cleanly.

Crawler settings used for this site:

• Worker processes: 32 (iNumberOfThreads = 32)
• Retries per URL: 10, with a 10-second socket timeout per attempt
• Proxy pool: not used — the original crawl connected directly (the published script can still route through proxies via SCRAPER_PROXIES, see below)
• User-agent rotation: yes, a random UA per request via fake_useragent
• Politeness delay: time.sleep(random.uniform(1.0, 3.5)) before every request
• Bot-challenge handling: responses containing #challenge-error-text or You don't have permission to access are discarded and retried after a short pause
• Raw HTML dump: enabled (raw/), used for debugging the parser
• Site-specific: /api/ URLs excluded

Paywall handling. The site serves its articles without a paywall, so no paywall filter was necessary.

2. Article extraction

get_content() reads the fields from the page's JSON-LD (<script type="application/ld+json">) and/or its HTML, as follows:

Field	Source in the page
title	headline from the ld+json block containing headline
content	articleBody from the ld+json block containing articleBody
author	author from ld+json — when it is a list, the last entry's name is used
description	description from ld+json
keywords	<meta name="keywords">, split on , and stripped
category	a.Breadcrumb_link__VP_Re entries whose href has more than two path segments (reverse-sorted, first)
datePublished	datePublished from ld+json
dateModified	dateModified from ld+json
url	the crawled URL

Each field is read from whichever ld+json block actually contains the corresponding key, because rtl.de emits several blocks per page. Pages without any ld+json, or with a block that does not parse, are skipped. The crawler additionally excludes /api/ URLs from the frontier.

Each article is stored as a JSON file of the shape

{
  "title": "…",
  "content": "…",
  "additional_attributes": { "author": "…", "description": "…", "keywords": "…", "category": "…", "datePublished": "…", "dateModified": "…", "url": "…" }
}

3. Consolidation — scraping/finalize.ipynb

1. glob("pages/*.json") collects every article file.
2. The nested records are flattened into one pandas.DataFrame column per field.
3. df.drop_duplicates(subset=['title']) removes duplicates — the same article is often reachable under several URLs (print/mobile variants, tracking parameters, section mirrors), and the headline is the most reliable identity key available.
4. The frame is written to rtl_de.json and pushed with datasets.Dataset.from_pandas(df).push_to_hub("SinclairSchneider/rtl_de").

Reproducing the crawl

pip install mechanicalsoup beautifulsoup4 lxml python-dateutil fake-useragent \
            pandas datasets huggingface_hub tqdm

mkdir rtl_de && cd rtl_de
curl -L -O https://huggingface.co/datasets/SinclairSchneider/rtl_de/resolve/main/scraping/crawler.py

python crawler.py          # writes pages/*.json; `touch killall.txt` to stop
jupyter notebook finalize.ipynb

Routing the crawl through your own proxies

The original crawl ran behind a pool of forward proxies on a private network. Those addresses are infrastructure-specific and have been removed from the published script. Instead, crawler.py reads the SCRAPER_PROXIES environment variable: a comma-separated list of proxies, written with or without a scheme (http:// is assumed when none is given). One entry is picked per request, so a pool spreads the load across several exits.

# a single proxy
export SCRAPER_PROXIES="proxy-a.example.com:3128"

# a pool, rotated per request
export SCRAPER_PROXIES="proxy-a.example.com:3128,proxy-b.example.com:3128"

# with an explicit scheme and credentials
export SCRAPER_PROXIES="http://user:pass@proxy.example.com:3128"

# SOCKS, if PySocks is installed
export SCRAPER_PROXIES="socks5://localhost:9050"

# back to a direct connection — this is the default
unset SCRAPER_PROXIES

python crawler.py

Internally this becomes requests' standard {'http': …, 'https': …} proxy mapping; with the variable unset the mapping is empty and the crawler connects directly. No credentials, cookies or personal data are contained in the published code.

Please crawl responsibly: respect robots.txt and the site's terms of use, keep the concurrency far below the 32 workers used here unless you have the site's permission, and prefer this published dataset over re-crawling.

Usage

from datasets import load_dataset
import pandas as pd

ds = load_dataset("SinclairSchneider/rtl_de", split="train")

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/rtl_de", split="train", streaming=True)
print(next(iter(stream))["title"])

# as a DataFrame
df = ds.to_pandas()
df["datePublished"] = pd.to_datetime(df["datePublished"], errors="coerce", utc=True, format="mixed")
recent = df[df["datePublished"].dt.year >= 2024]
print(len(recent), "articles from 2024 onwards")

Combining several outlets into one comparative corpus:

from datasets import load_dataset, concatenate_datasets

outlets = ["SinclairSchneider/rtl_de", "SinclairSchneider/tagesschau_de"]
parts = []
for name in outlets:
    d = load_dataset(name, split="train").select_columns(["title", "content", "url"])
    parts.append(d.add_column("outlet", [name.split("/")[-1]] * len(d)))
corpus = concatenate_datasets(parts)

Limitations and biases

• Crawl, not archive. Coverage is whatever was reachable by following links from the seed URL at crawl time. Articles that were deleted, depublished or orphaned before the crawl are missing, and old content is covered less densely than recent content.
• Free content only. All articles on this site are freely accessible, so no paywall bias applies — but non-article page types (galleries, video pages, live tickers) are mostly absent because they have no extractable body text.
• Deduplication by headline. Two genuinely different articles with an identical headline (common for recurring formats such as weather or daily briefings) collapse into one row.
• Noisy metadata. author, category and keywords come straight from the publisher's markup, which changed over the years; empty strings are common and category labels are not harmonised across outlets.
• Editorial perspective. This is the output of a single outlet. Any model trained on it alone inherits that outlet's selection of topics, framing and vocabulary — which is exactly why the collection provides the same pipeline across the political spectrum.
• No text normalisation. Bodies are the concatenated paragraphs as published, including typographic quotes, soft hyphens and occasional residual boilerplate.

Licensing and intended use

Scientific research only — Forschungsprivileg (§ 60d UrhG)

This dataset is made available solely for the purpose of scientific research. The legal basis is the German text-and-data-mining exception for research, the so-called Forschungsprivileg:

• § 60d UrhG — Text und Data Mining für Zwecke der wissenschaftlichen Forschung — allows research organisations to make reproductions of lawfully accessible works in order to carry out text and data mining for non-commercial scientific research, and to share the resulting corpus with a specifically limited circle of people for joint research and with individual third parties for the purpose of quality assurance.
• It implements Art. 3 of Directive (EU) 2019/790 (the DSM Directive); comparable research exceptions exist in the other EU member states.
• § 60d UrhG does not cover commercial exploitation. Research organisations that give a commercial enterprise preferential access are explicitly excluded from the privilege.

What this means for you

✅ Permitted — using the corpus for non-commercial scientific research: text and data mining, corpus linguistics, media-bias and discourse analysis, training and evaluating research models, reproducing published findings, and quality checks by individual third parties.

❌ Not permitted — commercial use or commercial model training; redistributing or republishing the article texts; presenting the texts as your own; building products or services on the article texts; any use that substitutes for reading the publisher's own website.

The article texts remain the copyright of RTL and its publisher. No licence to them is granted or implied by this repository. Only the crawling and extraction code in scraping/ is offered for free reuse.

If your intended use falls outside the research exception, obtain permission from the rights holder, or use only the derived, non-expressive data (statistics, embeddings, counts). Note that § 60d UrhG also expects research corpora to be deleted once they are no longer needed for the research purpose, with archiving permitted at trusted institutions.

If you are a rights holder and want content removed, please open a discussion on this repository.

This section is an orientation for researchers, not legal advice. Responsibility for your own use of the data remains with you and your institution.

Citation

@misc{rtl_de_dataset,
  title        = {RTL News (rtl.de): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/rtl_de}
}

See also

• The full German Media collection
• Mirror of this dataset: NLP-UniBW/rtl_de
• medienkompass.org — Deutsche Medienlandschaft, the source of the perceived-partisanship figures above