Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques

Jakub Piskorski, Nicolas Stefanovitch, Nikolaos Nikolaidis, Giovanni Da San Martino, Preslav Nakov


Abstract
We present a new multilingual multifacet dataset of news articles, each annotated for genre (objective news reporting vs. opinion vs. satire), framing (what key aspects are highlighted), and persuasion techniques (logical fallacies, emotional appeals, ad hominem attacks, etc.). 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 recent news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans of persuasion techniques. We describe the dataset and the annotation process, and we report the evaluation results of multilabel classification experiments using state-of-the-art multilingual transformers at different levels of granularity: token-level, sentence-level, paragraph-level, and document-level.
Anthology ID:
2023.acl-long.169
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3001–3022
Language:
URL:
https://aclanthology.org/2023.acl-long.169
DOI:
10.18653/v1/2023.acl-long.169
Bibkey:
Cite (ACL):
Jakub Piskorski, Nicolas Stefanovitch, Nikolaos Nikolaidis, Giovanni Da San Martino, and Preslav Nakov. 2023. Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3001–3022, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques (Piskorski et al., ACL 2023)
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