@inproceedings{icard-etal-2024-multi,
    title = "A Multi-Label Dataset of {F}rench Fake News: Human and Machine Insights",
    author = "Icard, Benjamin  and
      Maine, Fran{\c{c}}ois  and
      Casanova, Morgane  and
      Faye, G{\'e}raud  and
      Chanson, Julien  and
      Gadek, Guillaume  and
      Atemezing, Ghislain  and
      Bancilhon, Fran{\c{c}}ois  and
      {\'E}gr{\'e}, Paul",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.73",
    pages = "812--818",
    abstract = "We present a corpus of 100 documents, named OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text in the corpus. We then use the subjectivity analyzer VAGO, and a neural version of it, to clarify the link between ascriptions of the label Subjective and ascriptions of the label Fake News. The annotated dataset is available online at the following url: https://github.com/obs-info/obsinfox Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion, Exaggeration, French Press",
}

We present a corpus of 100 documents, named OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text in the corpus. We then use the subjectivity analyzer VAGO, and a neural version of it, to clarify the link between ascriptions of the label Subjective and ascriptions of the label Fake News. The annotated dataset is available online at the following url: https://github.com/obs-info/obsinfox Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion, Exaggeration, French Press
Icard, Benjamin Maine, Fran{\c{c}}ois Casanova, Morgane Faye, G{\'e}raud Chanson, Julien Gadek, Guillaume Atemezing, Ghislain Bancilhon, Fran{\c{c}}ois {\'E}gr{\'e}, Paul
A Multi-Label Dataset of French Fake News: Human and Machine Insights