  Most corpora approach misinformation as a binary problem, classifying texts
as real or fake. However, they fail to consider the diversity of existing
textual genres and types, which present different properties usually associated
with credibility. To address this problem, we created MINT, a comprehensive
corpus of news articles collected from mainstream and independent Portuguese
media sources, over a full year period. MINT includes five categories of
content: hard news, opinion articles, soft news, satirical news, and conspiracy
theories. This paper presents a set of linguistic metrics for characterization
of the articles in each category, based on the analysis of an annotation
initiative performed by online readers. The results show that (i) conspiracy
theories and opinion articles present similar levels of subjectivity, and make
use of fallacious arguments; (ii) irony and sarcasm are not only prevalent in
satirical news, but also in conspiracy and opinion news articles; and (iii)
hard news differ from soft news by resorting to more sources of information,
and presenting a higher degree of objectivity.
