README.md

metadata

license: cc-by-4.0
task_categories:
  - text-classification
  - video-classification
language:
  - de
tags:
  - tiktok
  - politics
  - social-media
  - germany
  - multimodal
  - content-moderation
size_categories:
  - 100K<n<1M

PoliTok-DE

A Multimodal Dataset of Political TikToks and Deletions From Germany.

Paper: PoliTok-DE: A Multimodal Dataset of Political TikToks and Deletions From Germany

Authors: Tomas Ruiz, Andreas Nanz, Ursula Kristin Schmid, Carsten Schwemmer, Yannis Theocharis, Diana Rieger

Dataset Description

This corpus contains multimodal data (video, audio, images, text) from TikTok posts related to the 2024 Saxony state election in Germany (July 1 – November 30, 2024). It includes over 195,000 posts, with more than 18,000 (17.3%) subsequently deleted from the platform.

Posts were identified through the TikTok Research API, with supplementary web scraping to obtain complete multimodal media and metadata.

Intended Uses

• Research on intolerance and political communication patterns
• Platform deletion policies and their implications
• Multimodal analysis of political content on social media

Dataset Access

This repository contains post IDs. Full content can be hydrated using the provided code. Access to deleted content requires approval for research purposes.

Hydration Code

The script hydrate.py must be in the same directory as the politok-de.parquet file. It will scrape the posts from the TikTok Website, and create for each post:

1. A video or image + audio file in the media/ directory.
2. An entry in the media_progress.csv file, stating if the post was successfully scraped.
3. An entry with post metadata in the media_metadata.csv file, only if the post was successfully scraped.

Install dependencies:

pip install -r requirements.txt

Run the script:

python hydrate.py

Note: To interrupt the script, you might need to press Ctrl+C multiple times due to the concurrent nature of the scraping code.

Citation

@article{ruiz2025politok,
  title={PoliTok-DE: A Multimodal Dataset of Political TikToks and Deletions From Germany},
  author={Ruiz, Tomas and Nanz, Andreas and Schmid, Ursula Kristin and Schwemmer, Carsten and Theocharis, Yannis and Rieger, Diana},
  journal={arXiv preprint arXiv:2509.15860},
  year={2025}
}