@inproceedings{icard-etal-2024-multi-label,
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",
}
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%0 Conference Proceedings
%T A Multi-Label Dataset of French Fake News: Human and Machine Insights
%A Icard, Benjamin
%A Maine, François
%A Casanova, Morgane
%A Faye, Géraud
%A Chanson, Julien
%A Gadek, Guillaume
%A Atemezing, Ghislain
%A Bancilhon, François
%A Égré, Paul
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F icard-etal-2024-multi-label
%X 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
%U https://aclanthology.org/2024.lrec-main.73
%P 812-818
Markdown (Informal)
[A Multi-Label Dataset of French Fake News: Human and Machine Insights](https://aclanthology.org/2024.lrec-main.73) (Icard et al., LREC-COLING 2024)
ACL
- Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, and Paul Égré. 2024. A Multi-Label Dataset of French Fake News: Human and Machine Insights. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 812–818, Torino, Italia. ELRA and ICCL.