SinclairSchneider/tweets_about_german_politicians_jan_feb_2025_reddit_and_telegram

---
license: mit
task_categories:
- text-classification
language:
- de
- en
tags:
- political-science
- social-media
- sentiment-analysis
- german-politics
- arxiv:2605.14354
size_categories:
- 1M<n<10M
---

# Dataset Card for LLM-based Detection of Manipulative Political Narratives

## Dataset Description

- **Paper:** [LLM-based Detection of Manipulative Political Narratives](https://arxiv.org/abs/2605.14354)
- **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}
}