论文标题
部分可观测时空混沌系统的无模型预测
Wikigender: A Machine Learning Model to Detect Gender Bias in Wikipedia
论文作者
论文摘要
Wikipedia的贡献者认为可以影响他们如何描述基于性别偏见的个人的方式。我们使用机器学习模型来证明如何在Wikipedia上描绘男女有所不同。此外，我们使用该模型的结果来获得哪些单词在英语Wikipedia的传记概述中产生偏见。我们仅将形容词作为模型的输入，我们表明用来描绘女性的形容词比描述男性的形容词具有更高的主观性。使用名词和形容词作为模型的输入从概述中提取主题，我们获得女性与家庭有关，而男性与商业和体育有关。
The way Wikipedia's contributors think can influence how they describe individuals resulting in a bias based on gender. We use a machine learning model to prove that there is a difference in how women and men are portrayed on Wikipedia. Additionally, we use the results of the model to obtain which words create bias in the overview of the biographies of the English Wikipedia. Using only adjectives as input to the model, we show that the adjectives used to portray women have a higher subjectivity than the ones used to describe men. Extracting topics from the overview using nouns and adjectives as input to the model, we obtain that women are related to family while men are related to business and sports.
