The development of explainable news credibility prediction models is critical both for fighting the viral propagation of misinformation and improving media literacy. This work investigates a variety of content indicators approaching different semantic and discourse dimensions, such as title representativeness, reasoning errors, and sentiment intensity. These indicators were inspired by a previous study conducted for English news, aimed at reaching a collective consensus on which indicators could be widely used for predicting news credibility. This new study, performed by a multi-disciplinary team, relies on a corpus of 80 news articles from Portuguese mainstream and alternative news media, which were annotated by junior and senior journalists. The assessment of the corpus annotations provides insight into the prevalence of different indicators in each type of news source. The results obtained for Portuguese correlate in most cases with the ones reported for English, which motivates the adoption of common standards for supporting the collaborative development of interoperable automatic misinformation detection approaches.