In this article, we present {\it GenderedNews}
(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.

GenderedNews 是一个在线仪表板，使用自然语言处理方法，以量化媒体中的性别不平等现象，其综合了以往定性和定量研究的手段，使用男性化率和引用的男性比例等指标进行度量，并针对其数据提供了可视化和分析工具。

GenderedNews: 用计算方法探究法国新闻中性别代表的差异

GenderedNews: Une approche computationnelle des écarts de représentation des genres dans la presse française

Contributing to the writing of history has never been as easy as it is today
thanks to Wikipedia, a community-created encyclopedia that aims to document the
world's knowledge from a neutral point of view. Though everyone can participate
it is well known that the editor community has a narrow diversity, with a
majority of white male editors. While this participatory \emph{gender gap} has
been studied extensively in the literature, this work sets out to \emph{assess
potential gender inequalities in Wikipedia articles} along different
dimensions: notability, topical focus, linguistic bias, structural properties,
and meta-data presentation.
We find that (i) women in Wikipedia are more notable than men, which we
interpret as the outcome of a subtle glass ceiling effect; (ii) family-,
gender-, and relationship-related topics are more present in biographies about
women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend
to be used to describe positive aspects in the biographies of men and negative
aspects in the biographies of women; and (iv) there are structural differences
in terms of meta-data and hyperlinks, which have consequences for
information-seeking activities. While some differences are expected, due to
historical and social contexts, other differences are attributable to Wikipedia
editors. The implications of such differences are discussed having Wikipedia
contribution policies in mind. We hope that the present work will contribute to
increased awareness about, first, gender issues in the content of Wikipedia,
and second, the different levels on which gender biases can manifest on the
Web.

评估维基百科文章中潜在的性别不平等，包括知名度、主题焦点、语言偏见、结构属性和元数据表现方面的差异。结果发现，维基百科中女性比男性更受关注，女性传记中更多有关家庭、性别和关系的主题，存在语言偏见和结构差异，需要注意性别问题对网络造成的各种差异。