PropaLTL at DIPROMATS 2024: Cross-lingual Data Augmentation for Propaganda Detection on Tweets

In this paper, we describe our participation in the Automatic Detection and Characterization of Propaganda Techniques and Narratives from Diplomats of Major Powers shared task (DIPROMATS). For this edition, we experimented with data augmentation, leveraging both English and Spanish training sets in a cross-lingual setting. As in the previous edition, the use of contextual features of the posts was also considered to improve their interpretation and subsequent classification. Our results show a slight increase in classification performance by incorporating more training instances. In particular, our strategy of cross-lingual data augmentation obtained competitive scores in the binary propaganda identification task: the eighth position for English out of 26 runs, and the eighth position for Spanish out of 30 runs. © 2024 Copyright for this paper by its authors.