Characterizing false information based on linguistic analysis is important to understand the factors that affect the proliferation of fake news in the media. Previous work has identified some linguistic regularities that suggest a trend towards decreased complexity, polarization and sentiment in false information. This study is aimed at identifying linguistic differences between real and fake news using a corpus of annotated media news in Spanish via the automatic analysis of linguistic cues using dictionaries of lexical norms. We focus on lexical aspects of complexity, familiarity and sentiment. Consistent with previous results, we found that fake news are associated with lower cognitive loads, reflected by reduced sentence complexity, and increased lexical familiarity and imaginability. Moreover, and consistently with previous results, the analysis revealed that fake news are associated with more polarized emotional content.