Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques
We present a new multilingual multi-facet dataset for understanding news (including "fake news''). Each document in the dataset is annotated in terms of genre (writing style used), framing (what key aspects are highlighted), and rhetoric (which persuasion techniques are used). The persuasion techniques are annotated at the span level, using a taxonomy of 23 fine-grained techniques grouped into 6 coarse categories. The dataset contains 1,612 news articles covering news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans. We describe the dataset and the annotation process, and we report on preliminary experiments aiming at multi-label classification using state-of-the-art multilingual transformers at different levels of granularity (sub-word, sentence, paragraph, document).
PISKORSKI Jakub;
STEFANOVITCH Nicolas;
NIKOLAIDIS Nikolaos;
GIOVANNI Da San Martino;
NAKOV Preslav;
2024-03-18
Association for Computational Linguistics (ACL)
JRC132614
https://aclanthology.org/2023.acl-long.169.pdf,
https://publications.jrc.ec.europa.eu/repository/handle/JRC132614,
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