Abstract
Acknowledging the importance of focusing on media’s communication for studying linguistic sexism, we propose a new method to analyze a corpus of texts via a machine learning approach built around an original training-set. We seek to establish a framework of the current use of talking about women in newspapers that expands beyond merely the objective forms of discrimination by also measuring the degree to which it implicitly conveys sexist messages through combination of words, expressions, and lexical aspects of language. As an illustrative example, we then apply such an approach to around 15,000 Italian newspapers’ headlines to investigate the impact of newspapers’ political orientations on the linguistic choices made by journalists in writing articles’ headlines.
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