Dataset Card: hplt-social-media-registers

This dataset contains 621,357 English, Finnish, and Swedish social media documents extracted from the HPLT 2.0 web corpus, enriched with web register labels and thematic subregister cluster labels. It was produced as part of Fin-CLARIAH deliverable D3.3.3 ("Machine Learning-Based Enrichment of Social Media") and is intended to support corpus linguistics research and downstream NLP on social media language varieties.

Dataset Details

Dataset Description

The dataset covers web documents with social media register characteristics from the CORE web register taxonomy, including Interactive Discussion (ID), Narrative Blog (NA-nb), and Opinion Blog (NA-nb-OP) labels, as well as various hybrid combinations of these (e.g. ds-IP-NA-nb, HI-NA-nb-re). Documents were automatically classified using TurkuNLP/web-register-classification-multilingual-bge and then clustered into thematic subregisters (e.g., sports, dining, culture) using HDBSCAN on BGE-M3 embeddings. Cluster labels were assigned manually by the dataset creators.

• Curated by: Erik Henriksson, Tuomas Lundberg, Veronika Laippala (TurkuNLP, University of Turku)
• Funded by: Fin-CLARIAH (Research Council of Finland, grant no. 358720)
• Language(s): English (en), Finnish (fi), Swedish (sv)
• License: CC BY 4.0
• Source corpus: HPLT 2.0 (CC0)

Dataset Sources

• Repository: https://github.com/TurkuNLP/social-media-enrichment-fin-clariah
• Paper: Henriksson et al. (2024), Automatic register identification for the open web using multilingual deep learning, arXiv:2406.19892
• Demo: Google Colab tutorial
• Deliverable overview: https://www.kielipankki.fi/organization/fin-clariah/deliverables/fin-clariah2024_d3-3-3/

Uses

Direct Use

• Corpus linguistics research on social media language variation (register, topic, style)
• Training or evaluating downstream classifiers for social media subregister detection
• Qualitative and quantitative analysis of thematic content in web discourse
• Use as a labeled reference corpus for Finnish, Swedish, and English social media text

Out-of-Scope Use

• This dataset covers only the social media subset of web registers; it is not representative of the full web register distribution
• The register and cluster labels are automatically assigned (ML-predicted), not manually verified at document level — do not treat them as gold-standard annotations
• Re-identification of individuals from the text is not an intended or appropriate use

Dataset Structure

Fields

Field	Type	Description
text	string	The document text
register	string	CORE web register label (hierarchical, e.g. NA-nb-OP)
embedding	float64[]	BGE-M3 document embedding used for clustering (1024 dimensions)
language	string	ISO 639-1 language code (en, fi, sv)
cluster_label	string	Thematic subregister label (e.g. sports, dining); empty string if not thematically labeled

Register and Cluster Label Taxonomy

Documents belong to one of the following register × language combinations, with the named cluster labels shown. An empty cluster_label means the document belongs to a cluster that was not given a thematic name.

Language	Register	Named cluster labels
en	ID	sports
en	NA-nb	comments
en	NA-nb-OP	culture, dining, lifestyle
fi	ID	sports
fi	ID-NA	comments
fi	NA-nb	comments
fi	NA-nb-OP	culture, consumption
fi	NA-nb-OP-rv	books, dining, beverages, cosmetics
sv	ds-IP-NA-nb	travel, contests
sv	HI-NA-nb-re	crafts
sv	ID	sports, help
sv	ID-NA-nb	comments
sv	IN-NA-nb	organizations
sv	NA-nb	comments
sv	NA-nb-OP	finance
sv	NA-nb-OP-rv	lifestyle, culture
sv	NA-ob-OP	sports

See the CORE register taxonomy documentation for the meaning of the hierarchical register labels.

Size

Language	Documents	With cluster label
English	91,633	4,559 (5.0%)
Finnish	261,688	32,372 (12.4%)
Swedish	268,036	30,919 (11.5%)
Total	621,357	67,850 (10.9%)

Dataset Creation

Curation Rationale

The dataset was created to provide a large, reusable labeled social media corpus, with a particular focus on Finnish and Swedish — languages underrepresented in social media NLP resources — alongside English for cross-linguistic comparison. The labels enable both coarse-grained register analysis (which broad type of social media?) and fine-grained thematic analysis (what is the text about?). The dataset is a key output of Fin-CLARIAH D3.3.3 and is intended for use in corpus linguistic research and NLP model development.

Source Data

Data Collection and Processing

Source documents were drawn from the HPLT 2.0 web crawl corpus for English, Finnish, and Swedish. The processing pipeline was:

1. Register classification: All documents were classified using TurkuNLP/web-register-classification-multilingual-bge, a multilingual BGE-M3-based web register classifier covering 25 CORE taxonomy labels.
2. Social media filtering: Documents predicted to belong to social media registers (Interactive Discussion, Narrative Blog, Opinion Blog, and their hybrids) were retained.
3. Embedding: Retained documents were embedded using BGE-M3 to produce dense vector representations.
4. Clustering: HDBSCAN clustering was applied to the embeddings within each register group to identify thematic subclusters.
5. Cluster labeling: Clusters were inspected manually and assigned human-readable thematic labels (e.g., sports, dining). Clusters that were too mixed or too small to label meaningfully were left with an empty cluster_label.

Who are the source data producers?

The source texts are web pages collected by the HPLT project via CommonCrawl. The original authors are the writers of those web pages. No demographic information about the source authors is available.

Annotations

Annotation process

Thematic cluster labels were assigned by the dataset curators (Erik Henriksson and Veronika Laippala) by inspecting cluster contents and selecting descriptive labels. Register labels were assigned automatically by the BGE-M3 classifier without manual verification.

Who are the annotators?

• Cluster label assignment: Erik Henriksson and Veronika Laippala (TurkuNLP)
• Register labels: Automatically predicted by TurkuNLP/web-register-classification-multilingual-bge
• Pipeline implementation: Tuomas Lundberg and Erik Henriksson (TurkuNLP)

Personal and Sensitive Information

The dataset is derived from publicly available web pages and may contain personal names, contact details, or other personally identifiable information consistent with the broader HPLT 2.0 corpus. No deliberate anonymization was applied beyond what is present in the HPLT 2.0 source.

Bias, Risks, and Limitations

• Automated labeling: Register labels are ML predictions, not manual annotations, and will contain some errors. Users should account for classifier noise, especially for documents with low-confidence predictions or hybrid register profiles.
• Language imbalance: Finnish (261,688) and Swedish (268,036) are roughly balanced, while English (91,633) is substantially smaller. Models trained on this data may generalize unevenly across languages.
• English cluster coverage: Only 5.0% of English documents have a non-empty cluster_label, compared to ~12% for Finnish and Swedish. Thematic subregister analysis is therefore much more limited for English.
• Register imbalance: Narrative Blog (NA-nb) accounts for ~79% of all documents (490,039 of 621,357). The remaining registers — Interactive Discussion, Opinion Blog, and hybrid types — are represented at much smaller scale.
• Cluster label skew: Among labeled documents, comments is by far the most frequent cluster label (47,961 of 67,850, ~71%). Other thematic labels such as travel, contests, and finance have fewer than 300 examples each.
• Web crawl quality: Source texts vary in quality, including near-duplicate content, boilerplate text, and encoding artefacts typical of web crawl corpora.
• Incomplete cluster coverage: Only 10.9% of documents have a thematic cluster label. Unlabeled clusters (cluster_label = "") exist where the thematic content was too heterogeneous or sparse to characterize.
• Register taxonomy scope: The CORE taxonomy's application to Finnish and Swedish may not capture all culturally specific text varieties.

Recommendations

Users should be aware that register labels are automatically predicted and not manually verified at document level. For high-stakes annotation tasks, downstream validation against manually labeled samples is recommended. The empty-string cluster label should be handled explicitly in any code that processes the cluster_label field.

Citation

If you use this dataset, please cite:

BibTeX:

@misc{henriksson2024automaticregisteridentification,
  title={Automatic register identification for the open web using multilingual deep learning},
  author={Erik Henriksson and Amanda Myntti and Anni Eskelinen and Selcen Erten-Johansson and Saara Hellstr{\"o}m and Veronika Laippala},
  year={2024},
  eprint={2406.19892},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2406.19892}
}

APA:

Henriksson, E., Myntti, A., Eskelinen, A., Erten-Johansson, S., Hellström, S., & Laippala, V. (2024). Automatic register identification for the open web using multilingual deep learning. arXiv:2406.19892.

Dataset Card Authors

Erik Henriksson, Tuomas Lundberg, Veronika Laippala (TurkuNLP, University of Turku)

Dataset Card Contacts

• Erik Henriksson — erikhenriksson (Hugging Face) | TurkuNLP, University of Turku
• Tuomas Lundberg — tuomaslundberg (Hugging Face) | TurkuNLP, University of Turku