---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
language:
- en
- fr
- de
- el
tags:
- populism
- politics
size_categories:
- 1K<n<10K
---


# Dataset Card

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/coastalcph/populism-eu-speeches
- **Repository:** https://github.com/coastalcph/populism-llms
- **Paper:** https://arxiv.org/abs/2507.19303
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)


## Dataset Summary

We curate, collect, clean, and annotate a collection of six speeches by five selected European leaders who recently ran for office and are widely considered to have relied on populist discourse during their campaigns. 
These are: Marine Le Pen (Rassemblement National, France), Alice Weidel (AfD, Germany), Herbert Kickl (FPÖ, Austria), Éric Zemmour (Reconquête, France), and Alexis Tsipras (SYRIZA, Greece). 
The first four politicians are identified as right-wing populists, while the last one is identified as a left-wing populist. 
Since the speeches are in the speaker's native language (French, German, and Greek), we autotranslate them to English using [DeepL API](https://www.deepl.com/en/pro-api) to bypass language limitations.


## Labeling / Annotation

The speeches in this set were split into separate sentences, which were annotated based on the 3-class labeling.
We used this dataset for evaluation purposes.


## Related Task

The goal of this task is to develop a method to identify fine-grained forms of populist discourse. 
Under our working definition, populism has two main components: *people-centrism* and *anti-elitism* that need to operate in tandem before we can label a discursive body (e.g. a political speech or a social media post) as a populist one. 
To enable a fine-grained analysis of political content and take advantage of higher-frequency data for examining variations in populist discourse, we move beyond full speeches or paragraphs as units of analysis and instead focus on a lower-level discursive structure: the sentence. 
We frame the task as a three-way multi-label classification, where each sentence can be labeled as neutral, anti-elitist, or people-centric. The co-occurrence of the latter two labels indicates a *fully populist* sentence.


## Statistics

| Category | Samples |
| -------- | -------- |
| Neutral (N) | 13,910 (92.6%) |
| Anti-elitism (AE) | 826 (5.5%) |
| People-centrism (PC) | 517 (3.4%) |

## Data Fields

- `speech_id`: a `string` with the speech identifier.
- `sentence`: a `string` with a sentence from a speech.
- `translated_sentence`: a `string` with a translated sentence from a speech.
- `pop_code`: an `integer` [0,1,2,3] identifying the category of the sentence.
- `speaker`: a `string` with the full name of the speaker.
- `location`: a `string` with the name of the location of the event.
- `date`:  a `string` with the full date (YYYY-MM-DD) of the speech.

## Data Instance / Example

```
{
'speech_id': '2014_05_22_tsipras.txt',
'sentence': 'Το πολιτικό προσωπικό των κομμάτων που κυβέρνησε αυτόν τον τόπο, από τη μεταπολίτευση και μετά, υπό τη στήριξη ολόκληρου του τραπεζικού και μιντιακού κατεστημένου, άφησε μια χώρα και έναν ολόκληρο λαό ξεκρέμαστο στις άγριες διαθέσεις των αγορών, στις άγριες διαθέσεις της κυρίας Μέρκελ.'
'translated_sentence': 'The political staff of the parties that have governed this country since the post-war period, with the support of the entire banking and media establishment, have left a country and an entire people exposed to the wild moods of the markets, to the wild moods of Mrs Merkel.',
'pop_code': 1,
'speaker': 'Alexis Tsipras'
'location': 'Athens, GR'
'date': '2014-05-22',
}
```

## How to use

```python
from datasets import load_dataset
train_dataset = load_dataset('coastalcph/populism-eu-speeches', split='train')
```

## Intended Usage

The dataset is intended for academic research for training and/or evaluating language models.

## Discussion of Biases

The dataset includes speeches of EU politicians from campaign rallies. 
It may contain derogatory language against protected groups based on race, gender, or other characteristics.
The authors do not endorse the use of such language.

## Citation Information

*[Identifying Fine-grained Forms of Populism in Political Campaign Speeches: A Case Study on Donald Trump’s Presidential Campaigns over a Decade (2015-2025). 
Ilias Chalkidis, Stephanie Brandl, and Paris Aslanidis. 
Arxiv Preprint, 2025.](https://arxiv.org/abs/2507.19303)*

```
@misc{chalkidis-et-al-2025-populism,
    title = "Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump’s Presidential Campaigns",
    author = "Chalkidis, Ilias and Brandl, Stephanie and Aslanidis, Paris",
    year = "2025",
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2507.19303}
}

```