File size: 3,779 Bytes
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dataset_info:
features:
- name: unique_ID
dtype: large_string
- name: ID
dtype: large_string
- name: text_ID
dtype: int64
- name: subcorpus
dtype: large_string
- name: author
dtype: large_string
- name: date
dtype: timestamp[ms]
- name: Text
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- name: text_tokenised
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- name: token_count
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- name: similar_IDs
list: string
- name: annotation_prompt
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- name: annotation_model
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- name: annotation_temperature
dtype: float64
- name: annotation_top_p
dtype: float64
- name: annotation_max_model_len
dtype: int64
- name: synthetic_labels
list:
- name: Aspekt
dtype: string
- name: Sentimentbegriff
dtype: string
- name: Valenz
dtype: string
- name: Erregung
dtype: string
- name: messages
list:
- name: role
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- name: content
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splits:
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num_bytes: 28892954
num_examples: 3934
- name: test
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num_examples: 463
- name: dev
num_bytes: 1751377
num_examples: 232
download_size: 12609414
dataset_size: 35883436
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
language:
- de
size_categories:
- 1K<n<10K
---
# AfD-CCC
A corpus of short texts and parliamentary speech transcripts by the German political party "Alternative für Deutschland" (AfD) addressing the topic of climate change (CC). The collection process and some exemplary usecases of the corpus are outlined in the associated paper and master's thesis. All data sources are clearly demarked in the following section.
## Data Sources
**Documents are taken from the original repository at [github.com/discourse-lab/afd-ccc](https://github.com/discourse-lab/afd-ccc/).**
> Manfred Stede and Ronja Memminger. 2025. [AfD-CCC: Analyzing the Climate Change Discourse of a German Right-wing Political Party.](https://aclanthology.org/2025.nlp4pi-1.14/) In Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI), pages 163–174, Vienna, Austria. Association for Computational Linguistics.
- German Parliament speeches (2017-2022): Extracted from [OpenDiscourse](https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/FIKIBO ) (Richter et al., 2020)
- German Parliament speeches (2022-2025): Retrieved from Government records via the [DIP API](https://dip.bundestag.de).
- European Parliament speeches (2014-2024): Extracted from the [ParlLawSpeech](https://parllawspeech.org/) dataset, Version 1.0 (Schwabach et al., 2025)
- Telegram messages (2019-2025): Scraped from public channels using [FROG](https://journals.sagepub.com/doi/10.1177/20501579241244973 ) (Priming & Fröschl, 2024)
- Press releases (2017-2021): Extracted from press releases made available by Schaefer et al. (2023)
- ~~Tweets (2017-2022): From Lasser et al. (2022). Due to Twitter privacy regulations, only tweet IDs can be given.~~ (Excluded due to missing documents.)
## Corpus Statistics
| **Text Domain** | **Texts** | **Tokens** | **Annotated texts** | **Total # synthetic labels** |
| :------------- | ----: | ------: | ------: | ------: |
| German Parl. | 2,284 | 220,894 | 2,197 | 12,332 |
| European Parl. | 668 | 48,025 | 659 | 3,017 |
| Press releases | 911 | 85,356 | 896 | 5,012 |
| ~~Twitter~~ | ~~1,880~~ | ~~54,592~~ | - | - |
| Telegram | 766 | 41,487 | 748 | 3,297 |
| **Total** | **4,629** | **395,762** | **4,500** | **23,658** | |