SinclairSchneider/los_angeles_times_com

Los Angeles Times (latimes.com)

684 740 English-language news articles crawled from www.latimes.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	www.latimes.com
Rows	684 740
Columns	9 (title, content, authors, description, keywords, category, datePublished, dateModified, url)
Language	English (en)
Split	train (single split)
Format	Parquet, 7 shards, ≈ 1,820 MB download / 2,930 MB in memory
Intended use	Scientific research only (Forschungsprivileg, § 60d UrhG)
Mirror	NLP-UniBW/los_angeles_times_com

from datasets import load_dataset

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

About the source

The Los Angeles Times is a daily newspaper published in Los Angeles and one of the largest metropolitan dailies in the United States. Founded in 1881, it has been owned since 2018 by the biotech entrepreneur Patrick Soon-Shiong through Nant Capital, after two decades under Tribune ownership. Its coverage combines California state and Los Angeles metro reporting with national politics, business, science and extensive entertainment-industry journalism.

Dataset structure

Single train split with 684 740 rows and the following columns:

Column	Type	Description	Extracted from
title	string	Headline of the article.	h1.headline
content	string	Full plain-text body of the article.	articleBody from ld+json (the full plain-text body supplied by the CMS)
authors	list[string]	—	the name values of author in ld+json, kept as a list
description	string	Teaser / meta description of the article.	description from ld+json
keywords	list[string]	Topic keywords / tags assigned by the publisher.	the tag links inside div.tags
category	string	Section the article was published in.	articleSection from ld+json
datePublished	string	Publication timestamp.	datePublished from ld+json
dateModified	string	Timestamp of the last editorial update.	dateModified from ld+json
url	string	Canonical URL the article was fetched from.	the crawled URL

Example record

{
  "title": "Beyond the Scalpel: What’s Really Changing in Surgical Oncology Around Brain Tumors",
  "content": "Evolving Techniques in Brain Tumor and Cancer Surgery Minimally Invasive Surgical Innovations for the Spine and Skull Base Advancements in Patient Care Other Oncologic Surgeries The Robot in the Room: A New Era of Surgical Procedures Surgical Resection Still Matters in Metastatic Disease A Broader Look at Innovation Training the Next Generation (And Thinking Beyond the Tumor) Closing Thoughts For  …",
  "authors": [
    "Kevin Famuyiro"
  ],
  "description": "Deconstructing the hype around new surgical tools to understand what truly improves patient outcomes in cancer resection today.",
  "keywords": [
    "Medical Advancements",
    "Medical Innovations"
  ],
  "category": "Medical Advancements",
  "datePublished": "2025-10-31T21:16:06.459Z",
  "dateModified": "2025-10-31T21:16:06.459Z",
  "url": "https://www.latimes.com/doctors-scientists/innovations/advancements/story/brain-tumor-surgery-advancements-surgical-oncology-innovations-vats"
}

Temporal coverage

The datePublished column could be parsed for 684 740 of 684 740 rows (100.0 %). Those rows span 1914-12-25 → 2025-11-01.

Year	Articles
1914	1	█
1915	1	█
1919	1	█
1923	1	█
1926	1	█
1927	3	█
1929	1	█
1930	1	█
1933	1	█
1935	1	█
1936	1	█
1942	1	█
1943	2	█
1944	1	█
1945	1	█
1949	1	█
1950	1	█
1955	2	█
1956	1	█
1957	2	█
1958	1	█
1959	3	█
1960	1	█
1961	3	█
1962	2	█
1964	2	█
1965	3	█
1966	5	█
1967	5	█
1968	1	█
1969	4	█
1971	1	█
1972	2	█
1973	2	█
1974	3	█
1975	2	█
1977	8	█
1978	2	█
1979	6	█
1980	6	█
1981	5	█
1982	4	█
1983	7	█
1984	6	█
1985	401	█
1986	453	█
1987	451	█
1988	476	█
1989	607	█
1990	658	█
1991	634	█
1992	655	█
1993	684	█
1994	721	█
1995	608	█
1996	591	█
1997	582	█
1998	664	█
1999	3 652	█
2000	8 105	███
2001	8 022	███
2002	11 217	████
2003	12 542	█████
2004	11 457	████
2005	10 723	████
2006	8 471	███
2007	9 676	████
2008	9 437	████
2009	8 594	███
2010	7 465	███
2011	10 047	████
2012	19 175	███████
2013	34 046	█████████████
2014	18 204	███████
2015	32 420	████████████
2016	41 280	████████████████
2017	56 561	█████████████████████
2018	54 620	█████████████████████
2019	63 664	████████████████████████
2020	50 634	███████████████████
2021	49 805	███████████████████
2022	42 302	████████████████
2023	33 638	█████████████
2024	32 527	████████████
2025	28 162	███████████

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.latimes.com/california/story/2025-10-31/horrific-details-emerge-as-usc-grad-student-confesses-to-police) 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.latimes.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. 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	h1.headline
content	articleBody from ld+json (the full plain-text body supplied by the CMS)
authors	the name values of author in ld+json, kept as a list
description	description from ld+json
keywords	the tag links inside div.tags
category	articleSection from ld+json
datePublished	datePublished from ld+json
dateModified	dateModified from ld+json
url	the crawled URL

Pages whose ld+json block does not parse are skipped. Because the body comes from the CMS-supplied articleBody field rather than the rendered HTML, it is free of navigation and advertisement fragments. author arrives either as a single object or as a list, and both shapes are normalised to a list of names.

Each article is stored as a JSON file of the shape

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

Dataset-specific notes

• The author column is named authors (plural) and holds a list, unlike the single-string author column used by most datasets in the German collection.

Reproducing the crawl

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

mkdir los_angeles_times_com && cd los_angeles_times_com
curl -L -O https://huggingface.co/datasets/SinclairSchneider/los_angeles_times_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:[email protected]: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/los_angeles_times_com", split="train")

# stream instead of downloading everything
stream = load_dataset("SinclairSchneider/los_angeles_times_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/los_angeles_times_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. 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 Los Angeles Times 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{los_angeles_times_com_dataset,
  title        = {Los Angeles Times (latimes.com): a corpus of German news articles},
  author       = {Schneider, Sinclair},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/SinclairSchneider/los_angeles_times_com}
}

See also

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