Add detailed dataset description

README.md

@@ -73,15 +73,69 @@ configs:
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 # Dataset Card for the benchmark Propaganda Dataset
 
-<!-- Provide a quick summary of the 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 1. In its current state, the dataset contains 8,646 doc-
+uments that were extracted from four Czech news websites. These
+websites were previously investigated for distributing Russian pro-
+paganda.
 
 
 ## 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