DIPROMATS 2023: Coarse propaganda characterization

The task aims at  finding the best techniques to identify and categorize propagandistic tweets from governmental and diplomatic sources on a  dataset of  tweets in Englsih, posted by authorities of China, Russia, United States and the European Union. The task seeks to classify the tweet into four clusters of propaganda techniques: appeal to commonality, discrediting the opponent, loaded language, appeal to authority.

Publication
Pablo Moral, Guillermo Marco, Julio Gonzalo, Jorge Carrillo-de-Albornoz, Iván Gonzalo-Verdugo (2023) Overview of DIPROMATS 2023: automatic detection and characterization of propaganda techniques in messages from diplomats and authorities of world powers. Procesamiento del Lenguaje Natural, Revista nº 71, septiembre de 2023, pp. 397-407.
Language
English
URL Task
https://sites.google.com/view/dipromats2023
NLP topic
text classification
Abstract task
Classification
Dataset
DIPROMATS 2023
Year
2023
Publication link
http://journal.sepln.org/sepl…
Ranking metric
ICM

Task results

System F1 Accuracy MacroF1 Pearson correlation ICM
Roberta large 0.5204 0.5204 0.5204 0.5204 0.52
Xlm roberta large 0.4867 0.4867 0.4867 0.4867 0.49
Roberta base 0.4811 0.4811 0.4811 0.4811 0.48
Bert base cased 0.4468 0.4468 0.4468 0.4468 0.45
Ixa ehu ixambert base cased 0.4430 0.4430 0.4430 0.4430 0.44
Xlm roberta base 0.4329 0.4329 0.4329 0.4329 0.43
Bert base multilingual cased 0.4266 0.4266 0.4266 0.4266 0.43
Distilbert base uncased 0.4054 0.4054 0.4054 0.4054 0.41
Distilbert base multilingual cased 0.3794 0.3794 0.3794 0.3794 0.38

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.