The task aims at finding the best techniques to identify and categorize propagandistic tweets from governmental and diplomatic sources on a dataset of 9501 tweets in Spanish, posted by authorities of China, Russia, United States and the European Union. The task consists on categorising propagandistic tweets in 15 propagandistic techniques.
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.
Task results
System | Precision | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM Sort ascending | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
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UniLeon | 0.90 | ||||||||||||||||||||||||||||||||||||
ELiRF-VRAIN | 0.90 | ||||||||||||||||||||||||||||||||||||
UMUTeam | 0.90 | ||||||||||||||||||||||||||||||||||||
UnedMBT | 0.89 | ||||||||||||||||||||||||||||||||||||
PropaLTL | 0.86 |