metadata

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
          dtype: int64
        - name: start
          dtype: int64
        - name: text
          dtype: string
  splits:
    - name: train
      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:

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?

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

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

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:

Sputnik News
Parlamentní listy (Parliamentary Letters)
AC24
Svět kolem nás (The World around Us).

Dataset Sources

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

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.