  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
