Nl

4 datasets tagged as the language “Nl”.

4 datasets.

Schwurbelarchiv: German Telegram Conspiracy Dataset Analyzed

A Telegram (German-language conspiracy channels) dataset of 63,908,365 messages from 4,404 unique channels, collected by Hagen, Wiencierz, and Freudenthaler via Telegram API. Each record contains message UUID, channel name, author, reply structure, conspiracy label, language, media type (photo/video/voice), audio/video duration, forwarding metadata, duplicate flag. Schwurbelarchiv is a large-scale German Telegram corpus with 3.1% of messages labeled as conspiracy content, 23.8% reply edges enabling thread analysis, and rich media metadata across 4,404 channels. Suitable for conspiracy theory detection, German NLP, social network analysis, Telegram platform research, misinformation propagation, reply network analysis, media sharing patterns.

63.9M records60.8M nodes7.3M edges2025

Hyves Friendship Network (December 2010) Analyzed

A Hyves (Dutch social network) dataset of 1,402,673 users and 2,777,419 friendship edges (avg degree 2.0), collected by Mahsa Mojtahedi via Hyves crawl (December 2010). Each record contains user-user friendship edges and user IDs. The dataset is notable for its very sparse friendship network from Hyves, the most popular social networking site in the Netherlands, competing with Facebook and MySpace. Intended for research into social network analysis, community detection, sparse network studies, and regional social media platform structure research.

1.4M records1.4M nodes2.8M edges2010

CLEF CheckThat! 2024 Task 1 — Dutch Check-Worthiness Analyzed

A CLEF CheckThat! 2024 shared task (dutch) dataset of 995 sentences with binary check-worthiness labels (40.7% check-worthy), collected by CLEF CheckThat! Lab organizers (2024). Each record contains sentence text, binary check-worthiness label (Yes/No), sentence ID, source document. CLEF 2024 CheckThat! Task 1 (dutch) provides 995 sentences from political debates and social media annotated for check-worthiness — whether the claim merits fact-checking. Suitable for check-worthiness detection, fact-checking automation, misinformation research, NLP classification benchmarks, political discourse analysis.

995 records2024

CLEF CheckThat! 2023 Subjectivity — Dutch Analyzed

A CLEF CheckThat! 2023 subjectivity task (dutch) dataset of 800 sentences with subjectivity labels (38.9% subjective), collected by CLEF CheckThat! Lab organizers (2023). Each record contains sentence text, binary subjectivity label (SUBJ/OBJ), sentence ID, conflict resolution flag. CLEF 2023 CheckThat! subjectivity (dutch) provides 800 news sentences annotated for subjectivity — whether the sentence expresses opinions (SUBJ) or facts (OBJ). Suitable for subjectivity detection, opinion mining, NLP classification, news objectivity analysis, multilingual text classification.

800 records2023