SinclairSchneider/vice_de

VICE Deutschland (vice.com/de)

53 831 German-language news articles crawled from www.vice.com/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.vice.com/de
Rows	53 831
Columns	8 (title, content, author, keywords, category, datePublished, dateModified, url)
Language	German (de)
Split	train (single split)
Format	Parquet, 1 shard, ≈ 177 MB download / 280 MB in memory
Perceived partisanship	2.8 / 7 → left of centre (medienkompass.org reader survey)
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/vice_de

from datasets import load_dataset

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

About the source

VICE is a youth-oriented media brand that started in 1994 as a magazine in Montreal and grew into an international digital publisher. The German-language edition at vice.com/de (which also serves Austria and Switzerland) publishes reportage, first-person stories, culture, music (formerly under the Noisey label), drugs, sexuality and politics, typically in an informal, subjective style. Vice Media went through Chapter 11 bankruptcy in 2023 and drastically scaled back its publishing in 2024, so the archive in this dataset is weighted towards earlier years.

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, Vice is rated:

	Score	Reading
Partisanship (1–7)	2.8	left of centre
Quality (1–5)	1.8	on the 1 = clickbait … 5 = complex scale

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

1         2         3         4         5         6         7
|---------|---------|---------|---------|---------|---------|
                  ^                                          
                Vice = 2.8

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 ⬅ this dataset	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 53 831 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	@graph[0].headline from the first ld+json block
content	string	Full plain-text body of the article.	all <p> inside div.entry-content, space-joined
author	string	Author(s) as published by the outlet.	<meta name="author">
keywords	list[string]	Topic keywords / tags assigned by the publisher.	@graph[0].keywords from the first ld+json block
category	string	Section the article was published in.	text of div.wp-block-savage-platform-post-primary-term (the primary topic label)
datePublished	string	Publication timestamp.	@graph[0].datePublished from the first ld+json block
dateModified	string	Timestamp of the last editorial update.	@graph[0].dateModified from the same block
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "“Ich möchte für das Schlechte stehen” – Männer müssen endlich über ihre Depressionen reden",
  "content": "Der Himmel war klar und die Rodelbahn vereist. Ich hatte meine Liebsten um mich, es war der letzte Tag des Jahres 2017. Ich rodelte so schnell wie noch nie und plötzlich flog ich mit gefühlt 60 Stundenkilometer durch die Luft. Ich merkte nur, wie meine Brille sich verabschiedete. Ich landete mit dem Gesicht am vereisten Boden. Ich sah nichts. Ich schmeckte nur Schnee, Schweiß und Blut. Nur eine le …",
  "author": "Christoph Schattleitner",
  "keywords": [
    "Burnout",
    "depression",
    "Feature",
    "Features",
    "Hilfe",
    "male depression",
    "Männer",
    "Neue Männlichkeit",
    "Schwäche",
    "suizid"
  ],
  "category": "",
  "datePublished": "2018-09-18T12:30:00+00:00",
  "dateModified": "2024-07-30T18:49:41+00:00",
  "url": "https://www.vice.com/de/article/ich-mochte-fur-das-schlechte-stehen-manner-mussen-endlich-uber-ihre-depressionen-reden/"
}

Temporal coverage

The datePublished column could be parsed for 53 831 of 53 831 rows (100.0 %). Those rows span 2004-11-01 → 2024-03-28.

Year	Articles
2004	1	█
2006	9	█
2007	157	█
2008	102	█
2009	339	█
2010	815	██
2011	1 214	██
2012	2 483	█████
2013	3 539	███████
2014	6 759	██████████████
2015	10 505	█████████████████████
2016	11 808	████████████████████████
2017	8 539	█████████████████
2018	4 265	█████████
2019	1 560	███
2020	672	█
2021	488	█
2022	293	█
2023	252	█
2024	31	█

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.vice.com/de/article/wie-ich-bei-einem-fetischdreh-eine-ungewohnliche-familie-kennenlernte/) 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.vice.com/de/ or with / (which are then made absolute) are added to the frontier. URLs containing # are dropped. Links containing /en/ (the English edition) are not followed.
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. 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	@graph[0].headline from the first ld+json block
content	all <p> inside div.entry-content, space-joined
author	<meta name="author">
keywords	@graph[0].keywords from the first ld+json block
category	text of div.wp-block-savage-platform-post-primary-term (the primary topic label)
datePublished	@graph[0].datePublished from the first ld+json block
dateModified	@graph[0].dateModified from the same block
url	the crawled URL

The site runs on WordPress with Yoast-style @graph JSON-LD; the first graph node is the article.

Each article is stored as a JSON file of the shape

{
  "title": "…",
  "content": "…",
  "additional_attributes": { "author": "…", "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 vice_de.json and pushed with datasets.Dataset.from_pandas(df).push_to_hub("SinclairSchneider/vice_de").

Site-specific consolidation steps for this dataset:

• After deduplication, rows whose content contains the English error text We’re sorry, the page you’re looking for could not be found are removed.

Dataset-specific notes

• There is no description column — the crawler does not extract one for this site.
• title comes straight from the JSON-LD and still contains HTML entities such as “ / ”; unescape it with html.unescape if needed.
• dateModified does not reflect editorial updates: all but 6 rows carry a timestamp between 2024-07-30 and 2025-03-11, left by a bulk migration of the archive. Use datePublished for any temporal analysis.

Reproducing the crawl

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

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

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/vice_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/vice_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 VICE 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{vice_de_dataset,
  title        = {VICE Deutschland (vice.com/de): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/vice_de}
}

See also

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