2021/08/13 by Danielle Caled, Caled, Danielle, Paula Carvalho +3
Computer Science · Social Sciences · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Misinformation and Its Impacts #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.06249
openalex publication_date 2021/08/13 · arxiv created 2021/10/18 · arxiv updated 2021/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.