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.
BibTeX
@inproceedings{piskorski-etal-2023-multilingual,
title = "Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques",
author = "Piskorski, Jakub and
Stefanovitch, Nicolas and
Nikolaidis, Nikolaos and
Da San Martino, Giovanni and
Nakov, Preslav",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.169/",
doi = "10.18653/v1/2023.acl-long.169",
pages = "3001--3022"
}