GenderedNews：法语报刊体裁表征的计算方法
GenderedNews: Une approche computationnelle des écarts de représentation des genres dans la presse française
摘要：
在这篇文章中，我们介绍了{ \it GenderedNews}（\url{https://gendered-news.imag.fr}），这是一个在线仪表板，每周提供法国在线新闻中性别不平衡的措施。我们使用自然语言处理（NLP）方法来量化媒体中的性别不平等，这是在全球媒体监测项目等全球项目之后。这些项目有助于突出媒体中的性别不平衡及其缓慢的演变。然而，由于抽样以及时间、数据和工作人员方面的费用，这些方法的推广受到限制。自动化使我们能够提供补充措施，以量化性别代表性的不平等。我们把代表性理解为新闻中提到和引用的男人和女人的存在和分布-而不是把代表性理解为陈规定型。本文首先回顾了以往媒体性别不平等研究所采用的不同方法：定性内容分析、定量内容分析和计算方法。然后，我们详细介绍了{ \it GenderedNews}采用的方法和实施的两个指标：男性提及率和在线新闻中引用的男性比例。我们描述了每天收集的数据（法国在线新闻媒体的七个主要标题）和我们的指标背后的方法，以及一些可视化。最后，我们建议通过对我们数据库中两个月的样本进行深入观察来说明对我们数据的可能分析。
英文摘要：
In this article, we present {\it GenderedNews} (\url{https://gendered-news.imag.fr}), an online dashboard which gives weekly measures of gender imbalance in French online press. We use Natural Language Processing (NLP) methods to quantify gender inequalities in the media, in the wake of global projects like the Global Media Monitoring Project. Such projects are instrumental in highlighting gender imbalance in the media and its very slow evolution. However, their generalisation is limited by their sampling and cost in terms of time, data and staff. Automation allows us to offer complementary measures to quantify inequalities in gender representation. We understand representation as the presence and distribution of men and women mentioned and quoted in the news -- as opposed to representation as stereotypification. In this paper, we first review different means adopted by previous studies on gender inequality in the media : qualitative content analysis, quantitative content analysis and computational methods. We then detail the methods adopted by {\it GenderedNews} and the two metrics implemented: the masculinity rate of mentions and the proportion of men quoted in online news. We describe the data collected daily (seven main titles of French online news media) and the methodology behind our metrics, as well as a few visualisations. We finally propose to illustrate possible analysis of our data by conducting an in-depth observation of a sample of two months of our database.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
