UniBO at CheckThat! 2024: Multi-lingual and Multi-label Persuasion Technique Detection in News with Data Augmentation and Sequence-Token Classifiers

Luca Giordano, Alberto Barrón-Cedeño

Abstract: With the widespread use of the Internet and the rise of algorithmic journalism, consumers of news are exposed more than ever before to manipulative, propagandistic, and deceptive content. As a result, major public events and debates on relevant topics can be significantly influenced. This creates an increasing demand for automated tools that help experts analyze the news ecosystem. We explored persuasion technique detection in multi-lingual news as part of the CheckThat! Lab Task 3. Our pipeline comprises two parts. The first part is a data augmentation module, which uses a BERT-based model fine-tuned for word-alignment to project labels from source texts to machine-translated target texts. The second one is a persuasion technique classification module, leveraging two fine-tuned BERT-based models: a sequence classifier for detecting sentences containing persuasion techniques and a set of 23 token-level classifiers for specific techniques. Our approach, trained on augmented multilingual data with class weighting and a high decision threshold of 0.9, is competitive in all language settings, showing hints of cross-lingual transfer. Despite the research efforts in this direction, exemplified by shared tasks, detecting persuasion techniques, especially across languages, remains challenging due to their implicit and subtle nature.