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.

中文翻译：

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性别歧视与媒体传播。意大利案例的应用


认识到关注媒体传播对于研究语言性别歧视的重要性，我们提出了一种新方法，通过围绕原始训练集构建的机器学习方法来分析文本语料库。我们试图建立一个框架，描述目前报纸上谈论女性的情况，该框架不仅限于客观形式的歧视，还通过单词、表达方式和语言词汇方面的组合来衡量它隐含地传达性别歧视信息的程度。作为一个说明性的例子，我们随后将这种方法应用于大约 15,000 份意大利报纸的标题，以调查报纸的政治倾向对记者在撰写文章标题时所做出的语言选择的影响。