Datasetpublic

SinclairSchneider/tweets_about_german_politicians_jan_feb_2025_reddit_and_telegram

Dataset Card for LLM-based Detection of Manipulative Political Narratives Dataset Summary This dataset comprises an unfiltered collection of 1,255,895 short social media posts. The language distribution is approximately 80% German and 20% English. Supported Tasks and Leaderboards text-classification sentiment-analysis Dataset Structure Data Instances A typical instance represents a single social media post… See the full description on the dataset page: https://huggingface.co/datasets/SinclairSchneider/tweets_about_german_politicians_jan_feb_2025_reddit_and_telegram.

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Dataset Card

Dataset Card for LLM-based Detection of Manipulative Political Narratives

Dataset Description

—Paper: LLM-based Detection of Manipulative Political Narratives
—Point of Contact: Sinclair Schneider

Dataset Summary

This dataset comprises an unfiltered collection of 1,255,895 short social media posts. The language distribution is approximately 80% German and 20% English.

Supported Tasks and Leaderboards

—text-classification
—sentiment-analysis

Dataset Structure

Data Instances

A typical instance represents a single social media post referencing a specific politician, alongside engagement statistics and sentiment scores:

json

{
  "source": "twitter",
  "id": "1874244910234561822",
  "text": "Bundeskanzler Olaf Scholz trifft heute den französischen Präsidenten in Paris zu Gesprächen über die europäische Sicherheit und wirtschaftliche Zusammenarbeit.",
  "author_id": "@NachrichtenAktuell",
  "negative": 0.0210,
  "neutral": 0.9540,
  "positive": 0.0250,
  "Name": "Olaf Scholz",
  "Partei": "SPD",
  "Land": "Brandenburg",
  "language": "de"
}

Paper: https://arxiv.org/abs/2605.14354

Citation

If you use this dataset in your research, please cite the foundational paper:

BibTeX:

bibtex

@misc{schneider2026llmbased,
      title={LLM-based Detection of Manipulative Political Narratives}, 
      author={Sinclair Schneider and Florian Steuber and Gabi Dreo Rodosek},
      year={2026},
      eprint={2605.14354},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}