A Multi-Label Dataset of French Fake News: Human and Machine Insights
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Icard, Benjamin Maine, François Casanova, Morgane Faye, Géraud Chanson, Julien Gadek, Guillaume Atemezing, Ghislain Bancilhon, François Égré, Paul |
| author_facet | Icard, Benjamin Maine, François Casanova, Morgane Faye, Géraud Chanson, Julien Gadek, Guillaume Atemezing, Ghislain Bancilhon, François Égré, Paul |
| contents | We present a corpus of 100 documents, 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-Label Dataset of French Fake News: Human and Machine Insights Icard, Benjamin Maine, François Casanova, Morgane Faye, Géraud Chanson, Julien Gadek, Guillaume Atemezing, Ghislain Bancilhon, François Égré, Paul Computation and Language Machine Learning We present a corpus of 100 documents, 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 |
| title | A Multi-Label Dataset of French Fake News: Human and Machine Insights |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.16099 |