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--- |
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language: |
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- cs |
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license: cc-by-nc-sa-4.0 |
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size_categories: |
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- 1K<n<10K |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: text |
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dtype: string |
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- name: genre |
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dtype: string |
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- name: topic |
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dtype: string |
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- name: scope |
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dtype: string |
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- name: location |
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dtype: string |
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- name: argumentation |
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dtype: string |
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- name: emotions |
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dtype: string |
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- name: overall_sentiment |
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dtype: string |
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- name: russia |
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dtype: string |
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- name: opinion |
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dtype: string |
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- name: expert |
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dtype: string |
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- name: source |
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dtype: string |
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- name: fear-mongering |
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dtype: string |
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- name: blaming |
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dtype: string |
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- name: labeling |
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dtype: string |
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- name: demonization |
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dtype: string |
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- name: relativization |
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dtype: string |
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- name: fabulation |
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dtype: string |
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- name: ranges |
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list: |
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- name: attribute |
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dtype: string |
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- name: end |
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dtype: int64 |
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- name: start |
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dtype: int64 |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 27173943 |
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num_examples: 7642 |
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- name: test |
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num_bytes: 3727325 |
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num_examples: 1000 |
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download_size: 19285049 |
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dataset_size: 30901268 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# Dataset Card for the benchmark Propaganda Dataset |
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Propaganda corpus is a joint work between multiple faculties of Masaryk University |
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(Faculty of Social Sciences, Faculty of Informatics, and Faculty of Law) |
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under the project Manipulative techniques of propaganda in the |
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age of Internet. In its current state, the dataset contains 8,646 documents |
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that were extracted from four Czech news websites. These |
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websites were previously investigated for distributing Russian propaganda. |
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## Dataset Details |
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Each document is annotated with three types of attributes: |
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1. **Manipulative techniques:** |
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- relate to specific sections of the document |
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| **Attribute** | **Classes** | **Description** | |
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| :--------- | :------- | :----------- | |
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| **Argumentation** | yes, no | Does the text present facts or arguments (logical, emotional, etc.) to support the main claim? | |
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| **Blaming**| yes, no | Does the text accuse someone of something? | |
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| **Demonization** | yes, no | Is the “enemy” and/or his/her goals or interests presented in the text as being evil | |
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| **Emotions** | grieviance, hatred, compassion, fear, missing | What is the main emotion the text is trying to evoke in the reader? | |
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| **Fabulation** | yes, no | Does the text contain unsubstantiated, overstated or otherwise incorrect claims? | |
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| **Fear-mongering** | yes, no | Is the text trying to appeal to fear, uncertainty or other threat? | |
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| **Labeling** | yes, no | The text uses specific labels – short and impactful phrases or words – to describe a person, group or object. | |
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| **Relativization** | yes, no | Are the presented actions of a person, group or party being relativized? | |
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2. **Global attributes:** |
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| **Attribute** | **Classes** | **Description** | |
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| :--------- | :------- | :----------- | |
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| **Genre** | news, comment, interview | The publication form of the news text. | |
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| **Location** | EU, Czech Republic, USA, Russia, NATO, Russia + USA, other locations, other/cannot be determined | What is the main location the text discusses about? | |
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| **Overall Sentiment** | positive, negative, neutral | The core sentiment of the newspaper text. | |
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| **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 | |
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| **Scope** | foreign, domestic, both, cannot be determined | Distinguishes domestic and foreign topics | |
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3. **Other attributes:** |
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- do no fit into any other categories (they relate to a specific section of a document but are not manipulative techniques by themselves) |
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| **Attribute** | **Classes** | **Description** | |
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| :--------- | :------- | :----------- | |
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| **Expert** | yes, no | Is the text or opinion in the text presented as being supported by an expert? | |
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| **Opinion** | yes, no | Does the author of the text present his or her personal opinion? | |
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| **Russia** | positive example, neutral, victim, negative example, hero, missing | How Russia is depicted in the article? | |
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| **Source** | yes, no | Is the text presented as being based on a specific source? | |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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The benchmark Propaganda dataset contains 8,646 newspaper articles from 2016 (5,500 documents, 2,7 million tokens), |
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2017 (1,994 documents, 930 thousand tokens), and 2018 (1,152 documents, 500 thousand tokens). Compared with other resources, |
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the Propaganda dataset contains fine-grained annotations of both document-level attributes and specific text devices exemplified |
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by marked phrases from the article texts. |
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The Czech Republic was selected here as a representative of a country within the former Soviet Union influence and, as such, |
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with significantly active propaganda sources. The analyzed news texts were downloaded from four newspaper media outlets publishing in the Czech language: |
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1. Sputnik News |
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2. Parlamentní listy (Parliamentary Letters) |
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3. AC24 |
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4. Svět kolem nás (The World around Us). |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** https://nlp.fi.muni.cz/projects/propaganda/dataset/ |
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- **Paper:** https://dx.doi.org/10.1016/j.eswa.2024.124085 |
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- **Demo:** https://nlp.fi.muni.cz/projects/propaganda |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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@article{horak_etal2024_recognition, |
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title = {Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis}, |
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author = {Aleš Horák and Radoslav Sabol and Ondřej Herman and Vít Baisa}, |
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journal = {Expert Systems with Applications}, |
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pages = {124085}, |
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year = {2024}, |
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issn = {0957-4174}, |
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publisher = {Elsevier}, |
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doi = {https://doi.org/10.1016/j.eswa.2024.124085}, |
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} |
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**APA:** |
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Aleš HORÁK, Radoslav SABOL, Ondřej HERMAN and Vít BAISA. |
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Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis. |
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Expert Systems with Applications. Elsevier, 2024. ISSN 0957-4174. https://dx.doi.org/10.1016/j.eswa.2024.124085. |
