---
language:
- cs
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: genre
    dtype: string
  - name: topic
    dtype: string
  - name: scope
    dtype: string
  - name: location
    dtype: string
  - name: argumentation
    dtype: string
  - name: emotions
    dtype: string
  - name: overall_sentiment
    dtype: string
  - name: russia
    dtype: string
  - name: opinion
    dtype: string
  - name: expert
    dtype: string
  - name: source
    dtype: string
  - name: fear-mongering
    dtype: string
  - name: blaming
    dtype: string
  - name: labeling
    dtype: string
  - name: demonization
    dtype: string
  - name: relativization
    dtype: string
  - name: fabulation
    dtype: string
  - name: ranges
    list:
    - name: attribute
      dtype: string
    - name: end
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    - name: start
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    - name: text
      dtype: string
  splits:
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    num_bytes: 27173943
    num_examples: 7642
  - name: test
    num_bytes: 3727325
    num_examples: 1000
  download_size: 19285049
  dataset_size: 30901268
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
# Dataset Card for the benchmark Propaganda Dataset

Propaganda corpus is a joint work between multiple faculties of Masaryk University
(Faculty of Social Sciences, Faculty of Informatics, and Faculty of Law) 
under the project Manipulative techniques of propaganda in the
age of Internet. In its current state, the dataset contains 8,646 documents 
that were extracted from four Czech news websites. These
websites were previously investigated for distributing Russian propaganda.


## Dataset Details



Each document is annotated with three types of attributes:
1. **Manipulative techniques:**
 - relate to specific sections of the document
| **Attribute** | **Classes** | **Description** |
| :--------- | :------- | :----------- |
| **Argumentation** | yes, no | Does the text present facts or arguments (logical, emotional, etc.) to support the main claim? |
| **Blaming**| yes, no | Does the text accuse someone of something? |
| **Demonization** | yes, no | Is the “enemy” and/or his/her goals or interests presented in the text as being evil |
| **Emotions** | grieviance, hatred, compassion, fear, missing | What is the main emotion the text is trying to evoke in the reader? |
| **Fabulation** | yes, no | Does the text contain unsubstantiated, overstated or otherwise incorrect claims? |
| **Fear-mongering** | yes, no | Is the text trying to appeal to fear, uncertainty or other threat? |
| **Labeling** | yes, no | The text uses specific labels – short and impactful phrases or words – to describe a person, group or object. |
| **Relativization** | yes, no | Are the presented actions of a person, group or party being relativized? |


2. **Global attributes:**
| **Attribute** | **Classes** | **Description** |
| :--------- | :------- | :----------- |
| **Genre** | news, comment, interview | The publication form of the news text. |
| **Location** | EU, Czech Republic, USA, Russia, NATO, Russia + USA, other locations, other/cannot be determined | What is the main location the text discusses about? |
| **Overall Sentiment** | positive, negative, neutral | The core sentiment of the newspaper text. |
| **Topic** | migration crisis, domestic politics, foreign policy / diplomacy, society / social situation, energy, economy / finance, conflict in Ukraine, conflict in Syria, conspiracy, other, culture, social policy, arms policy | various topics |
| **Scope** | foreign, domestic, both, cannot be determined | Distinguishes domestic and foreign topics |



3. **Other attributes:**
 - do no fit into any other categories (they relate to a specific section of a document but are not manipulative techniques by themselves)
| **Attribute** | **Classes** | **Description** |
| :--------- | :------- | :----------- |
| **Expert** | yes, no | Is the text or opinion in the text presented as being supported by an expert? |
| **Opinion** | yes, no | Does the author of the text present his or her personal opinion? |
| **Russia** | positive example, neutral, victim, negative example, hero, missing | How Russia is depicted in the article? |
| **Source** | yes, no | Is the text presented as being based on a specific source? |

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
The benchmark Propaganda dataset contains 8,646 newspaper articles from 2016 (5,500 documents, 2,7 million tokens),
2017 (1,994 documents, 930 thousand tokens), and 2018 (1,152 documents, 500 thousand tokens). Compared with other resources,
the Propaganda dataset contains fine-grained annotations of both document-level attributes and specific text devices exemplified
by marked phrases from the article texts.

The Czech Republic was selected here as a representative of a country within the former Soviet Union influence and, as such,
with significantly active propaganda sources. The analyzed news texts were downloaded from four newspaper media outlets publishing in the Czech language:
1. Sputnik News
2. Parlamentní listy (Parliamentary Letters)
3. AC24
4. Svět kolem nás (The World around Us). 

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://nlp.fi.muni.cz/projects/propaganda/dataset/
- **Paper:** https://dx.doi.org/10.1016/j.eswa.2024.124085
- **Demo:** https://nlp.fi.muni.cz/projects/propaganda


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

    @article{horak_etal2024_recognition,
      title = {Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis},
      author = {Aleš Horák and Radoslav Sabol and Ondřej Herman and Vít Baisa},
      journal = {Expert Systems with Applications},
      pages = {124085},
      year = {2024},
      issn = {0957-4174},
      publisher = {Elsevier},
      doi = {https://doi.org/10.1016/j.eswa.2024.124085},      
    }
    
**APA:**

  Aleš HORÁK, Radoslav SABOL, Ondřej HERMAN and Vít BAISA. 
  Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis. 
  Expert Systems with Applications. Elsevier, 2024. ISSN 0957-4174. https://dx.doi.org/10.1016/j.eswa.2024.124085.