SinclairSchneider/sueddeutsche_de

Süddeutsche Zeitung (sueddeutsche.de)

1 887 355 German-language news articles crawled from www.sueddeutsche.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.sueddeutsche.de
Rows	1 887 355
Columns	13 (title, content, author, description, keywords, category, about, dateCreated, datePublished, dateModified, wordCount, alternativeHeadline, url)
Language	German (de)
Split	train (single split)
Format	Parquet, 10 shards, ≈ 2,736 MB download / 4,517 MB in memory
Perceived partisanship	3.5 / 7 → relatively neutral (medienkompass.org reader survey)
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/sueddeutsche_de

from datasets import load_dataset

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

About the source

The Süddeutsche Zeitung (SZ) is a national daily newspaper from Munich, founded in 1945 as one of the first newspapers licensed by the US military government in Bavaria. It is published by the Süddeutscher Verlag, which belongs to the Südwestdeutsche Medien Holding (SWMH). The SZ is one of Germany's leading quality papers, known for its investigative unit (e.g. the Panama Papers), and is usually described as left-liberal. Besides national and international coverage, sueddeutsche.de carries a large amount of regional reporting from Munich and Bavaria. A large part of the site sits behind the SZ Plus paywall — only freely accessible articles are contained in this dataset.

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, Süddeutsche is rated:

	Score	Reading
Partisanship (1–7)	3.5	relatively neutral
Quality (1–5)	3.3	on the 1 = clickbait … 5 = complex scale

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

1         2         3         4         5         6         7
|---------|---------|---------|---------|---------|---------|
                         ^                                   
                    Süddeutsche = 3.5

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 ⬅ this dataset	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	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 1 887 355 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	headline from the first ld+json block
content	string	Full plain-text body of the article.	articleBody from the same block (the full plain-text body supplied by the CMS)
author	string	Author(s) as published by the outlet.	<meta data-manual="author">
description	string	Teaser / meta description of the article.	description from ld+json
keywords	list[string]	Topic keywords / tags assigned by the publisher.	keywords from ld+json, split on ,
category	string	Section the article was published in.	articleSection from ld+json
about	string	Topic label the publisher assigned to the article.	about from ld+json
dateCreated	string	Timestamp at which the CMS record was created.	dateCreated from ld+json
datePublished	string	Publication timestamp.	datePublished from ld+json
dateModified	string	Timestamp of the last editorial update.	dateModified from ld+json
wordCount	int64	Publisher's own word-count annotation.	wordCount from ld+json (the publisher's own length annotation)
alternativeHeadline	string	Kicker / overline printed above the headline.	alternativeHeadline from ld+json
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Terminkalender",
  "content": "Wolfratshausen\nLesung \"Fiktion vs. Fakten\".  Im Rahmen der \"Klima-Lesestaffel 2024\". Krimi-Autor Martin Arz stellt sich Hauptkommissar Ludwig Waldinger - und umgekehrt! Eintritt 12 Euro inkl. 1 Glas Wein. Reservierung erforderlich unter 08171/764 55, keine Abendkasse. Stadtbücherei, Hammerschmiedweg 3; Montag, 19 Uhr.\nProgramm für ehrenamtliche rechtliche Betreuer.  Veranstalter: Betreuungsstelle  …",
  "author": "",
  "description": "",
  "keywords": [
    "Bad Tölz-Wolfratshausen",
    "Landkreis Bad Tölz-Wolfratshausen",
    "Süddeutsche Zeitung"
  ],
  "category": "Bad Tölz-Wolfratshausen",
  "about": "Bad Tölz-Wolfratshausen",
  "dateCreated": "2024-04-21T11:59:50.000Z",
  "datePublished": "2024-04-21T11:59:50.000Z",
  "dateModified": "2024-04-21 11:59:50",
  "wordCount": 1807,
  "alternativeHeadline": "Terminkalender",
  "url": "https://www.sueddeutsche.de/muenchen/wolfratshausen/heute-und-morgen-terminkalender-1.6563771"
}

Temporal coverage

The datePublished column could be parsed for 1 887 355 of 1 887 355 rows (100.0 %). Those rows span 2002-07-19 → 2025-06-10.

Year	Articles
2002	1	█
2003	1	█
2004	3	█
2005	10	█
2006	11	█
2007	10	█
2008	19	█
2009	9	█
2010	49	█
2011	1 131	█
2012	4 102	█
2013	35 212	████
2014	99 889	████████████
2015	95 707	███████████
2016	94 171	███████████
2017	129 160	███████████████
2018	190 985	██████████████████████
2019	198 837	███████████████████████
2020	203 592	████████████████████████
2021	207 671	████████████████████████
2022	207 750	████████████████████████
2023	191 829	██████████████████████
2024	162 908	███████████████████
2025	64 298	███████

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.sueddeutsche.de/politik/italien-opposition-meloni-volksabstimmungen-li.3265512) 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.sueddeutsche.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/, .jpg, .pdf or .epub, or when it has fewer than five path segments (which filters out section index pages).
5. Fetching. Each URL gets up to 1000 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: 1000, with a 10-second socket timeout per attempt
• Proxy pool: used — a pool of forward proxies on a private network, switching to the next proxy every 60 seconds (lproxies[int(time.time()/60) % len(lproxies)]); the published script applies the same rotation to the pool given in SCRAPER_PROXIES
• User-agent rotation: no, MechanicalSoup's default UA
• Politeness delay: none — throttling was handled by the proxy pool / retry loop
• Bot-challenge handling: none
• Raw HTML dump: disabled

Paywall handling. Pages that contain div[data-paycategory="paid"] (SZ Plus) are skipped, as are pages whose first ld+json block cannot be parsed.

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 first ld+json block
content	articleBody from the same block (the full plain-text body supplied by the CMS)
author	<meta data-manual="author">
description	description from ld+json
keywords	keywords from ld+json, split on ,
category	articleSection from ld+json
about	about from ld+json
dateCreated	dateCreated from ld+json
datePublished	datePublished from ld+json
dateModified	dateModified from ld+json
wordCount	wordCount from ld+json (the publisher's own length annotation)
alternativeHeadline	alternativeHeadline from ld+json
url	the crawled URL

Because the body text comes from the CMS-supplied articleBody field rather than from the rendered HTML, the text is free of navigation, teaser and advertisement fragments.

Each article is stored as a JSON file of the shape

{
  "title": "…",
  "content": "…",
  "additional_attributes": { "author": "…", "description": "…", "keywords": "…", "category": "…", "about": "…", "dateCreated": "…", "datePublished": "…", "dateModified": "…", "wordCount": "…", "alternativeHeadline": "…", "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 sueddeutsche_de.json and pushed with datasets.Dataset.from_pandas(df).push_to_hub("SinclairSchneider/sueddeutsche_de").

Dataset-specific notes

• wordCount is the publisher's own figure and is not recomputed from content.
• dateModified can be written as YYYY-MM-DD HH:MM:SS without a time zone, unlike the ISO-8601 timestamps with Z in datePublished / dateCreated; parse it separately if you compare the columns.

Reproducing the crawl

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

mkdir sueddeutsche_de && cd sueddeutsche_de
curl -L -O https://huggingface.co/datasets/SinclairSchneider/sueddeutsche_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/sueddeutsche_de", split="train")

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/sueddeutsche_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/sueddeutsche_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. Paywalled articles were deliberately skipped, so the dataset under-represents long-form and investigative pieces, which are the ones most often placed behind the paywall.
• 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 Süddeutsche Zeitung 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{sueddeutsche_de_dataset,
  title        = {Süddeutsche Zeitung (sueddeutsche.de): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/sueddeutsche_de}
}

See also

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