@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",
    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.",
}

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.
Piskorski, Jakub Stefanovitch, Nicolas Nikolaidis, Nikolaos Da San Martino, Giovanni Nakov, Preslav
Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques