SinclairSchneider/neues_deutschland_de

nd / neues deutschland (nd-aktuell.de)

283 405 German-language news articles crawled from www.nd-aktuell.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.nd-aktuell.de
Rows	283 405
Columns	11 (title, content, author, description, keywords, category, subcategory, dateCreated, datePublished, dateModified, url)
Language	German (de)
Split	train (single split)
Format	Parquet, 2 shards, ≈ 506 MB download / 816 MB in memory
Perceived partisanship	2.6 / 7 → left of centre (medienkompass.org reader survey)
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/neues_deutschland_de

from datasets import load_dataset

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

About the source

nd (neues deutschland, since 2022 published as nd.DerTag / nd.DieWoche) is a German left-wing daily newspaper from Berlin. Founded in 1946, it was the central organ of the SED in the GDR; since 1990 it has appeared as an independent socialist paper and has been run as a co-operative since 2022. Its archive covers both the post-reunification period and current left-wing political debate.

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, neues Deutschland is rated:

	Score	Reading
Partisanship (1–7)	2.6	left of centre
Quality (1–5)	2.3	on the 1 = clickbait … 5 = complex scale

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

1         2         3         4         5         6         7
|---------|---------|---------|---------|---------|---------|
                ^                                            
        neues Deutschland = 2.6

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 ⬅ this dataset	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	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 283 405 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	<meta property="og:title">
content	string	Full plain-text body of the article.	all <p> inside div.Content, space-joined
author	string	Author(s) as published by the outlet.	<meta name="author">
description	string	Teaser / meta description of the article.	<meta property="og:description">
keywords	string	Topic keywords / tags assigned by the publisher.	<meta name="keywords">
category	string	Section the article was published in.	a.Active inside ul.Menu (the highlighted section in the main navigation)
subcategory	string	Sub-section or kicker below the main section.	div.Deck (the kicker/deck line above the headline)
dateCreated	string	Timestamp at which the CMS record was created.	dateCreated, read out of the raw ld+json text
datePublished	string	Publication timestamp.	datePublished, read out of the raw ld+json text
dateModified	string	Timestamp of the last editorial update.	dateModified, read out of the raw ld+json text
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Newsblog: +++ »Wir enteignen euch alle« +++",
  "content": "Update 21:26 Uhr: Kühnert will BMW kollektivieren\r\nAn dieser Stelle beenden wir unseren Newsblog rund um die Ereignisse am 1. Mai, wollen euch aber eine letzte, interessante Meldung nicht vorenthalten: Juso-Chef Kevin Kühnert möchte große Firmen eigenen Worten zufolge kollektivieren. In einem »Zeit«-Interview sagte er, er wolle eine Kollektivierung von Unternehmen wie BMW »auf demokratischem Wege« …",
  "author": "Redaktion nd-aktuell.de",
  "description": "Tag der Arbeit: Nach der Demo ist bekanntlich vor der Demo. Während der Protest in Berlin-Grunewald inzwischen vorbei ist, gehört der Abend traditionell der »Revolutionären 1. Mai Demonstration«. Die aktuellen Entwicklungen im Newsblog.",
  "keywords": "1. Mai, AfD, Antifa, DGB, Gewerkschaft, LINKE, linke Bewegung, Nazis",
  "category": "Politik & Ökonomie",
  "subcategory": "Politik 1. Mai",
  "dateCreated": "2019-05-01T10:26:55+0200",
  "datePublished": "2019-05-01T10:26:55+0200",
  "dateModified": "2023-01-21T15:23:37+0100",
  "url": "https://www.nd-aktuell.de//artikel/1117739.mai-newsblog-wir-enteignen-euch-alle.html"
}

Temporal coverage

The datePublished column could be parsed for 39 317 of 283 405 rows (13.9 %). Those rows span 2001-10-26 → 2025-06-13.

Year	Articles
2001	2	█
2002	6	█
2003	1	█
2004	3	█
2005	8	█
2006	2	█
2007	1	█
2008	4	█
2009	68	█
2010	233	█
2011	215	█
2012	178	█
2013	183	█
2014	202	█
2015	251	█
2016	360	█
2017	703	██
2018	938	██
2019	970	███
2020	1 672	████
2021	2 919	████████
2022	9 163	████████████████████████
2023	8 719	███████████████████████
2024	8 627	███████████████████████
2025	3 889	██████████

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.nd-aktuell.de/artikel/1191840.antimuslimischer-rassismus-muslime-in-berlin-unter-generalverdacht.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.nd-aktuell.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, .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 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 — requests were sent through a rotating pool of forward proxies (the published script reproduces this via SCRAPER_PROXIES, see below)
• User-agent rotation: no, MechanicalSoup's default UA
• Politeness delay: time.sleep(random.uniform(1.0, 3.5)) before every request
• Bot-challenge handling: none
• Raw HTML dump: enabled (raw/), used for debugging the parser

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	<meta property="og:title">
content	all <p> inside div.Content, space-joined
author	<meta name="author">
description	<meta property="og:description">
keywords	<meta name="keywords">
category	a.Active inside ul.Menu (the highlighted section in the main navigation)
subcategory	div.Deck (the kicker/deck line above the headline)
dateCreated	dateCreated, read out of the raw ld+json text
datePublished	datePublished, read out of the raw ld+json text
dateModified	dateModified, read out of the raw ld+json text
url	the crawled URL

The three timestamps are extracted by string-splitting the raw ld+json text rather than by parsing it, because the block on this site is not always valid JSON. The crawler also uses a much higher retry budget (1000 attempts per URL instead of 10), since the site responded slowly during the crawl.

Each article is stored as a JSON file of the shape

{
  "title": "…",
  "content": "…",
  "additional_attributes": { "author": "…", "description": "…", "keywords": "…", "category": "…", "subcategory": "…", "dateCreated": "…", "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 neues_deutschland_de.json and pushed with datasets.Dataset.from_pandas(df).push_to_hub("SinclairSchneider/neues_deutschland_de").

Dataset-specific notes

• keywords is a single comma-separated string, not a list.

Reproducing the crawl

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

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

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/neues_deutschland_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/neues_deutschland_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 nd 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{neues_deutschland_de_dataset,
  title        = {nd / neues deutschland (nd-aktuell.de): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/neues_deutschland_de}
}

See also

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