DIPROMATS 2023

Automatic Detection and Characterization of Propaganda Techniques from Diplomats of Major Powers

Shared task at IberLEF 2023

Welcome to DIPROMATS, the shared task on propaganda and narrative detection at the 2023 Iberian Languages Evaluation Forum  (IberLEF)


This NLP challenge asks participants to develop systems able to detect and characterize propagandistic techniques in tweets written by diplomats from USA, Europe, Russia and China in English and Spanish

Call for participation

Unlike fake news, the detection of propaganda in news and social media has not attracted so much attention from journalists, fact-checkers, or scholars. In our view, this hinders the endeavors against hostile and manipulative information. The deceiving intent of propaganda may be more subtle and devious than disinformation; its content does not have to be false, and its effects may be only discernible through systematic observation over time. 

As a means by which certain ideas and actions propagate, propaganda involves rhetorical techniques to improve replication. This task proposes a specific approach to detect those techniques based on the language employed by official authorities on Twitter. The corpus provided for the task encompasses tweets in Spanish and English from diplomats of four different international actors: China, Russia, United States, and the European Union. The authorities collected include government accounts, embassies, ambassadors, and other diplomatic profiles such as consuls and missions.   


This shared task challenges participants to classify tweets according to the following two tasks:

 

Task 1: propaganda identification: The first subtask is a binary classification problem. The systems must decide whether a given tweet contains propaganda techniques.  


Task 2: propaganda characterization: The second subtask aims to categorize the type of propaganda. The proposed categorization considers multiple techniques identified in literature that are clustered according to their rhetorical features. We propose a multiclass, multilabel classification task, where systems have to decide, for each tweet, in which of the available categories it fits. The proposed typology can be found here.  Evaluation will consider a coarse grain categorization with four classes of propaganda (plus a negative class), and a fine-grained categorization with 15 subclasses (plus a negative class). 


We encourage participation from both academic institutions and industrial organizations. To participate in the task, please fill the registration form at https://sites.google.com/view/dipromats2023/registration  


Important Dates: 


Organizers:  

Pablo Moral, Universidad Nacional de Educación a Distancia (UNED) 

Guillermo Marco, Universidad Nacional de Educación a Distancia (UNED) 

Julio Gonzalo, Universidad Nacional de Educación a Distancia (UNED) 


Contact: 

If you have any questions or need more information, please do not hesitate to contact us at dipromats@lsi.uned.es .