Skip to content


Version: 1.0.0


license: Not publicly available.

DaNews consists of articles from Danish news and tabloid media from 1 December 2019 to 30 April 2021. The articles stem from multiple news sources, including both online of physical newspapers.

DaNews consists of 403 million tokens 93% were left after quality filtering and deduplication.


Following the recommendation and framework of [5] we add the following datasheet.


For what purpose was the dataset created? Who created the dataset? Who funded the creation of the dataset?

DANews was collected as a part of the HOPE project, examining news coverage during the COVID-19 pandemic. The purpose was to train a model to understand how the novelty and resonance imprint of COVID-19 as a case of crisis compared to non-crises news imprints.

Any other comments?



How many instances are there in total (of each type, if appropriate)?

The unfiltered dataset consists of 713 429 documents including a total of 403 089 625 tokens.

What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?

Instances of the dataset are Danish articles derived from Danish tabloids or news media.

Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?

Prior to filtering DaNews dataset contains all digitized news articles from the given period across the sources.

What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.

Each instance consists of the following columns

'ArticleUrl', 'Heading', 'SubHeading', 'Lead', 'Paragraph', 'PublishDate', 'BodyText', 
'Captions', 'Authors', 'Source', 'WordCount', 'ArticleId', 'PageIds', 'Section', 'text'

Where we constructed the columns text column by joining the Heading, SubHeading using newline. If the text field is empty it is ignored and no newline is added. The we join the resulting string with the BodyText using two newlines.

During the quality filtering, we add the following indicator columns:

'passed_quality_filter', 'filtered_by_max_chr_length', 'filtered_by_doc_length', 
'filtered_by_mean_word_length', 'filtered_by_alpha_ratio', 'filtered_by_stop_word', 
'filtered_by_symbol_2_word_hashtag', 'filtered_by_symbol_2_word_ellipsis',
'filtered_by_line_bullets_or_ellipsis', 'filtered_by_duplicate_lines_chr_fraction',
'filtered_by_duplicate_paragraph_chr_fraction', 'filtered_by_top_ngram_chr_fraction',
'filtered_by_duplicate_ngram_chr_fraction', 'is_duplicate'

Is there a label or target associated with each instance? If so, please provide a description.


Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information but might include, e.g., redacted text.

The team of researchers at the Humanities Computing Aarhus (CHCAA) have not removed any information from the instances.

Are relationships between individual instances made explicit (e.g., users’ movie ratings, and social network links)? If so, please describe how these relationships are made explicit.

The metadata columns denote the relationship between articles including the date of publication, sections, and authors.

Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

There are not splits performed on this dataset.

Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.

News sources can publish their content both in an online and printed format which would lead to similar instances in the dataset. To alleviate this redundancy by removing near-duplicates (see Preprocessing/cleaning/labeling).

Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?

Articles are intended to tell a self-contained story but can include external references such as tweets or website URLs.

Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?

Articles often describe content that is considered offensive, insulting, or threatening.

Collection Process

What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)?

A team of researchers at the Center for Humanities Computing Aarhus (CHCAA) obtained this dataset using a third-party API as well as a manual transfer from one of the parties. The API was limited to only a subset of articles agreed upon within the agreements.

If the dataset is a sample from a larger set, what was the sampling strategy?

The dataset is not a sample, but is a filtered version of the full dataset, see Preprocessing/cleaning/labeling for more on this.

Who was involved in the data collection process? A team of researchers at the Center for Humanities Computing Aarhus (CHCAA) obtained this dataset using a third party API as well as a manual transfer from some of the parties and would like to thank the dataset owners for access to their articles.

Over what timeframe was the data collected?

The dataset includes articles from 1 December 2019 to 30 April 2021.

Were any ethical review processes conducted?



Was any preprocessing/Cleaning/Labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?

DaNews has been filtered using a series of heuristic filters as well as removing repetitious texts. Following the filtering, DaNews is deduplicated to remove exact and near-duplicates.

Of all documents, 9% were filtered based due to low-quality and 4% because they were near-duplicates.

For quality filtering, DaNews applies a filter akin to [2] which contains text that:

  • Contain at least 2 Danish stopwords. For the stopword list we use the one used in SpaCy v.3.1.4.
  • Have a mean word length between 3 and 10.
  • Have a token length between 50 and 100,000.
  • Have less than 5,000,000 characters.
  • Have less than 60% of words containing an alphabetic character.
  • Have a symbol-to-word ratio lower than 10% for hashtags and ellipsis.
  • Have less than 90% of lines starting with a bullet point.
  • have less than 30% of lines ending with an ellipsis.

  • Have a low high degree of repetitious text:

  • Have less than 20% of characters contained within duplicate lines.
  • Have less than 20% of characters contained within duplicate paragraphs.
  • Where the top 2-4 grams constitute less than 20%, 18%, 16%, respectively, of the text.
  • Where the duplicate 5-10 grams constitute less than 25%, 24%, 23%, 22%, 21%, 20% of the text, respectively.

The deduplication removed all documents with a 13-gram Jaccard similarity higher than 80% following the MinHash algorithm [1] using 128 permutations. The MinHash algorithm is a probabilistic data structure for approximating the Jaccard similarity between two sets.

Is the software used to preprocess/clean/label the instances available?

Yes, the scripts are available here. the scripts use version 0.0.2 of the dfm package.


Has the dataset been used for any tasks already?

Yes, the dataset has been used to pre-train Danish language models. Parts of the dataset have also been used in [3] and [4]

Is there a repository that links to any or all papers or systems that use the dataset?


What (other) tasks could the dataset be used for?

The scale of the dataset makes it suitable for NLP tasks such as language modeling. Similarly, the structure of the articles makes it a suitable dataset for training text summarisation models.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?

This dataset is static and thus does not evolve over time with the language. A consequence of this is that it will become increasingly outdated over time.

Are there tasks for which the dataset should not be used?

This dataset contains Danish articles and thus should not be used for non-Danish language tasks.

As the writers of the content are predominantly journalists, it reflects a certain writing style which is unlikely to reflect the Danish language as a whole.


Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created?

Data will only be available at the entity during the project. If you wish access to the dataset you will have to come to an agreement with the individuals Danish newspapers.


If you wish to cite this work please see our GitHub page for an up-to-date citation:


  • [1] Broder, Andrei Z. "On the resemblance and containment of documents." Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171). IEEE, 1997.
  • [2] Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., Rutherford, E., Hennigan, T., Menick, J., Cassirer, A., Powell, R., Driessche, G. van den, Hendricks, L. A., Rauh, M., Huang, P.-S., … Irving, G. (2021). Scaling Language Models: Methods, Analysis & Insights from Training Gopher.
  • [3] Baglini, R. B., Nielbo, K. L., Hæstrup, F., Enevoldsen, K., Vahlstrup, P. B., & Roepstorff, A. (2021, June 2). When no news is bad news: Detection of negative events from news media content.
  • [4] Nielbo, K. L., Baglini, R. B., Vahlstrup, P. B., Enevoldsen, K. C., Bechmann, A., & Roepstorff, A. (2021, January). News information decoupling: An information signature of catastrophes in legacy news media.
  • [5] T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford. Datasheets for datasets. arXiv preprint arXiv:1803.09010, 2018.