SinclairSchneider/achgut_de

Die Achse des Guten (achgut.com)

72 119 German-language news articles crawled from www.achgut.com 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.achgut.com
Rows	72 119
Columns	7 (title, content, author, description, category, datePublished, url)
Language	German (de)
Split	train (single split)
Format	Parquet, 1 shard, ≈ 181 MB download / 284 MB in memory
Perceived partisanship	5.2 / 7 → right of centre (medienkompass.org reader survey)
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/achgut_de

from datasets import load_dataset

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

About the source

Die Achse des Guten ("Achgut") is a German political group blog founded in 2004 by the journalists Henryk M. Broder and Dirk Maxeiner together with Michael Miersch and others. It publishes commentary, essays and short "Fundstücke" by a large pool of regular and guest authors. The blog describes itself as liberal and pro-Western; it is sharply critical of government policy on migration, climate, energy and the Covid-19 pandemic, and media researchers usually place it in the conservative-libertarian to right-wing populist spectrum. The output is predominantly opinion rather than news reporting, which should be taken into account when the corpus is compared with newsroom outlets.

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, Achgut is rated:

	Score	Reading
Partisanship (1–7)	5.2	right of centre
Quality (1–5)	2.4	on the 1 = clickbait … 5 = complex scale

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

1         2         3         4         5         6         7
|---------|---------|---------|---------|---------|---------|
                                          ^                  
                                       Achgut = 5.2

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	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 ⬅ this dataset	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 72 119 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	first <h2> inside div#article_maincontent
content	string	Full plain-text body of the article.	all <p> inside div#article_maincontent, space-joined
author	string	Author(s) as published by the outlet.	text of div#author_header
description	string	Teaser / meta description of the article.	<meta name="description">
category	string	Section the article was published in.	<meta property="og:type">
datePublished	string	Publication timestamp.	the bare text nodes of div.teaser_text_meta (the date line under the headline), with tabs, newlines and / removed
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Beugt Trump das Recht gegen unliebsame Ausländer?",
  "content": "Präsident Donald Trump versucht Schnellabschiebungen mit einem Gesetz aus einem „Sicherheitspaket“ von 1798. Das für die Abschiebungen passende Gesetz wurde aber schon 1800 gestrichen. Hat er solche Mittel nötig? Am 4. März 1801 geschah in Washington, D.C. etwas in der Welt fast Unerhörtes: Ein friedlicher, planvoller und geordneter Regierungswechsel in einem großen Staat aufgrund einer Wahl durch …",
  "author": "Oliver M. Haynold",
  "description": "Präsident Donald Trump versucht Schnellabschiebungen mit einem Gesetz aus einem „Sicherheitspaket“ von 1798. Das für die Abschiebungen passende Gesetz dieses Pakets wurde aber schon 1800 gestrichen. Hat er solche Mittel nötig?",
  "category": "article",
  "datePublished": "27.03.2025 12:00",
  "url": "https://www.achgut.com/artikel/beugt_Trump_das_recht_gegen_unliebsame_Auslaender"
}

Temporal coverage

The datePublished column could be parsed for 72 118 of 72 119 rows (100.0 %). Those rows span 2006-10-07 → 2025-06-12.

Year	Articles
2006	368	██
2007	3 953	█████████████████
2008	4 577	████████████████████
2009	4 764	█████████████████████
2010	4 557	████████████████████
2011	4 559	████████████████████
2012	3 859	█████████████████
2013	3 865	█████████████████
2014	2 891	█████████████
2015	2 684	████████████
2016	2 988	█████████████
2017	2 939	█████████████
2018	3 075	█████████████
2019	3 983	█████████████████
2020	4 142	██████████████████
2021	5 483	████████████████████████
2022	3 414	███████████████
2023	3 487	███████████████
2024	4 600	████████████████████
2025	1 930	████████

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 nine URLs: two article pages, the weekly overview https://www.achgut.com/woche and its pages P10 … P50, and a search page of the article archive (that last one contains # and is therefore skipped by the URL filter of the published revision). Only the first seed (https://www.achgut.com/artikel/ukraine_krieg_weder_wendepunkt_noch_waffenstillstand) is configurable through SCRAPER_START_URL; the others are hard-coded in main().
3. Link discovery. Every fetched page is parsed with BeautifulSoup/lxml; all <a href> values that start with https://www.achgut.com/ or with / (which are then made absolute) are added to the frontier. URLs containing # are dropped. In the published revision get_urls() itself keeps # links; they are removed by the URL filter in the next step instead.
4. URL filtering. Before fetching, a URL is skipped when it contains /files/, /video/, .jpg, .pdf, #, app-eu.readspeaker.com or .epub.
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 single forward proxy on a private network (selected through lproxies[int(time.time()/10) % len(lproxies)], which would rotate every 10 seconds over a larger pool); the published script applies the same expression to the pool given in SCRAPER_PROXIES
• User-agent rotation: yes, a random UA per request via fake_useragent, sent together with a fixed set of browser-like Accept / Sec-Fetch-* headers
• Politeness delay: time.sleep(random.uniform(1.0, 3.5)) before every request
• Bot-challenge handling: responses containing the site's error page (Fehlernummer: 1047) are retried after 1 s, responses containing You don't have permission to access this resource. after 10 s
• Raw HTML dump: enabled (raw/), used for debugging the parser
• Site-specific: no minimum path depth filter

Paywall handling. The crawler applies no paywall filter — every fetched page with a non-empty article body was kept.

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	first <h2> inside div#article_maincontent
content	all <p> inside div#article_maincontent, space-joined
author	text of div#author_header
description	<meta name="description">
keywords	<meta name="keywords">, split on , — extracted by the crawler but not carried into the dataset
category	<meta property="og:type">
datePublished	the bare text nodes of div.teaser_text_meta (the date line under the headline), with tabs, newlines and / removed
url	the crawled URL

The site exposes no usable JSON-LD, so every field is read from the rendered HTML. datePublished is the German date string shown on the page (dd.mm.yyyy HH:MM), not ISO-8601 — parse it with dayfirst=True.

Each article is stored as a JSON file of the shape

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

Dataset-specific notes

• category is the page's Open Graph type (og:type), not an editorial section, and is article for every row — it carries no information.
• keywords was extracted by the crawler but dropped by finalize.ipynb, so the dataset has no keyword column.
• Two crawler revisions. The crawl folder holds two revisions of the script. The earlier one (not published) sent all requests through a local Tor SOCKS proxy and paused while a helper script requested a new Tor circuit once a minute; it did not filter URLs containing #. The published crawler.py is the later revision achgut_com_2.py. The dataset contains pages fetched by both: 13 732 rows (19 %) have a url with a fragment (e.g. …/P7#com, a comment page), which only the earlier revision fetched. Dedup by headline keeps one row per article either way, but the url of such a row may point to a comment page rather than to the article's canonical address.
• The published revision still imports fp.fp.FreeProxy and fetches one free proxy at start-up (hence the free-proxy dependency). That value is overwritten before every request by the proxy configured through SCRAPER_PROXIES (or a direct connection), so it is never actually used; the line is kept so that the script stays exactly as it was run.
• description contains HTML entities such as   taken verbatim from the meta tag.

Reproducing the crawl

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

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

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/achgut_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", dayfirst=True)
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/achgut_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.
• No paywall filter. No paywall filter was applied: if the site served a truncated teaser instead of a full article, that teaser was kept as it was. 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 Achse des Guten 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{achgut_de_dataset,
  title        = {Die Achse des Guten (achgut.com): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/achgut_de}
}

See also

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