Rap music is one of the most influential contemporary music genres with a huge societal significance. Existing analyses of rap often rely on surveys or web scraping approaches that do not take popularity of songs into account. In this work, we contribute a novel dataset for German rap lyrics analysis, comprising 13,997 songs that achieved high chart positions between the years 2015-2023. Each song is further annotated with moderation labels, reflecting the presence of harassment, violence, sexuality, hate and self-harm, enabling analysis of harmful or explicit content within German rap. The proposed dataset opens up the possibility to explore individual artists' lyrical styles as well as the temporal development of German rap.

Create python venv with python 3.10 and install requirements:

python3.10 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Copy the file .env.base to .env and fill in the secrets.

GENIUS_TOKEN='REPLACE_ME'
OPENAI_API_KEY='REPLACE_ME'

The main script for scraping the data can be found in src/main.py.

Run the script with:

python src/main.py

The scraped data is stored in the data folder:

• data/csv/charts.csv: Dataset with the scraped charts data.
• data/csv/songs.csv: Dataset with the scraped songs data (no lyrics).
• data/csv/moderation.csv: Dataset with the content moderation predictions.
• data/csv/output.csv: Joined dataset of the above datasets.
• data/lyrics.zip: Zip file with the scraped lyrics.

The experiments are stored in the experiments folder:

• experiments/exp_PD_001_AlgorithmicAnalysis.ipynb: Notebook with the algorithmic analysis of the moderation labels.
• experiments/exp_PD_002_DeToxAnalysis.ipynb: Notebook with the analysis of the DeTox hate speech labels.

Results of the experiments are stored in the data/experiments folder.

The scripts for creating visualizations for the paper can be found in doc/data_lit_23-24_paper/fig.