SinclairSchneider/euobserver_com

EUobserver (euobserver.com)

742 English-language news articles crawled from euobserver.com and published as a Hugging Face dataset. This is part of the International Newspapers collection, a set of comparable corpora covering English-language newspapers and EU-affairs 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	euobserver.com
Rows	742
Columns	8 (title, content, author, description, keywords, category, datePublished, url)
Language	English (en)
Split	train (single split)
Format	Parquet, 1 shard, ≈ 3 MB download / 4 MB in memory
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/euobserver_com

from datasets import load_dataset

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

About the source

EUobserver is a Brussels-based online newspaper reporting on European Union affairs. Founded in 2000 by Lisbeth Kirk, it is run as a non-profit and has historically positioned itself as an independent, institution-critical counterweight to the Brussels press corps, financed through a mix of subscriptions, foundation grants and donations. Its newsroom is small, so output volume is far lower than the other outlets in this collection.

Dataset structure

Single train split with 742 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 <span style="white-space: pre-wrap;"> blocks except the final two, space-joined
author	string	Author(s) as published by the outlet.	the second-to-last <span style="white-space: pre-wrap;"> block
description	string	Teaser / meta description of the article.	<meta name="description">
keywords	list[string]	Topic keywords / tags assigned by the publisher.	the underlined tag <div>s next to the <h1>
category	string	Section the article was published in.	the first path segment of the article URL
datePublished	string	Publication timestamp.	<meta property="article:published_time">
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Behind the scam: how an alleged criminal network set a trap for thousands of Europeans to fall for ‘fake’ investments and raked in millions",
  "content": "At first glance, Olympus Prime looked more like a thriving tech start-up than one of the numerous, non-descript call centres popping up across Serbia’s capital city. Housed in a high-spec design building amongst a growing business neighborhood in Belgrade, the company shared space with a Chinese cultural complex and an upscale hotel. Yet when Serbian police entered the premises of the Olympus Prim …",
  "author": "Meanwhile, many victims still grapple with what happened to them. Some of those approached for this investigation refused to speak, afraid they might be targeted by scam agencies again. For others, the memories were simply too painful to revisit. \"It is the worst thing to talk about,\" says one woman who lost €54,000 to Greenfields Capital, “because as a victim, you are trying to forget it.”",
  "description": "A suspected scam network operating out of Serbia duped an estimated 70,000 people worldwide into making bogus investments, profiting €250m in the process. The transnational scheme involved a former Premier League team sponsor and a convicted money launderer, Investigate Europe and the Balkan Investigative Reporting Network can reveal, in the first part of our Scam Europe series.    ",
  "keywords": [
    "EU & the World",
    "Investigations",
    "Health & Society"
  ],
  "category": "eu-and-the-world",
  "datePublished": "2025-09-30T06:00:00.000Z",
  "url": "https://euobserver.com/eu-and-the-world/arf605a31b"
}

Temporal coverage

The datePublished column could be parsed for 742 of 742 rows (100.0 %). Those rows span 2023-01-02 → 2025-10-28.

Year	Articles
2023	1	█
2024	233	███████████
2025	508	████████████████████████

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://euobserver.com/digital/ar9b098635) 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://euobserver.com 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 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: used — requests were sent through a rotating pool of forward proxies (the published script reproduces this via SCRAPER_PROXIES, see below)
• User-agent rotation: yes, a random UA per request via fake_useragent
• Politeness delay: none — throttling was handled by the proxy pool / retry loop
• 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

Paywall handling. Articles showing the subscriber notice To read this story, log in or subscribe in an <h4> are skipped.

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 <span style="white-space: pre-wrap;"> blocks except the final two, space-joined
author	the second-to-last <span style="white-space: pre-wrap;"> block
description	<meta name="description">
keywords	the underlined tag <div>s next to the <h1>
category	the first path segment of the article URL
datePublished	<meta property="article:published_time">
url	the crawled URL

EUobserver's CMS renders the article body as a flat run of white-space: pre-wrap spans whose last two entries are the byline and a trailing notice. The parser therefore takes everything except the final two spans as the body and the second-to-last span as the author.

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 euobserver_com.json and pushed with datasets.Dataset.from_pandas(df).push_to_hub("SinclairSchneider/euobserver_com").

Dataset-specific notes

• This is a small dataset. The positional span heuristic above only matches EUobserver's current article template, and subscriber-only stories are skipped, so coverage is a fraction of the site's archive.
• author and category are positional/URL-derived rather than read from structured metadata, so they are noisier than in the other datasets of this collection.

Reproducing the crawl

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

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

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/euobserver_com", 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/euobserver_com", "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 EUobserver 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{euobserver_com_dataset,
  title        = {EUobserver (euobserver.com): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/euobserver_com}
}

See also

• The full International Newspapers collection
• Mirror of this dataset: NLP-UniBW/euobserver_com