Data from: Women are seen more than heard in online newspapers

Feminist news media researchers have long contended that masculine news values shape journalists’ quotidian decisions about what is newsworthy. As a result, it is argued, topics and issues traditionally regarded as primarily of interest and relevance to women are routinely marginalised in the news, while men’s views and voices are given privileged space. When women do show up in the news, it is often as “eye candy,” thus reinforcing women’s value as sources of visual pleasure rather than residing in the content of their views. To date, evidence to support such claims has tended to be based on small-scale, manual analyses of news content. In this article, we report on findings from our large-scale, data-driven study of gender representation in online English language news media. We analysed both words and images so as to give a broader picture of how gender is represented in online news. The corpus of news content examined consists of 2,353,652 articles collected over a period of six months from more than 950 different news outlets. From this initial dataset, we extracted 2,171,239 references to named persons and 1,376,824 images resolving the gender of names and faces using automated computational methods. We found that males were represented more often than females in both images and text, but in proportions that changed across topics, news outlets and mode. Moreover, the proportion of females was consistently higher in images than in text, for virtually all topics and news outlets; women were more likely to be represented visually than they were mentioned as a news actor or source. Our large-scale, data-driven analysis offers important empirical evidence of macroscopic patterns in news content concerning the way men and women are represented.

长期以来，女性主义新闻媒体研究学者始终主张，男性化的新闻价值观主导着记者日常判定新闻价值的决策过程。据此，相关研究指出，传统上被认为主要契合女性兴趣、与女性息息相关的话题与议题，在新闻报道中常遭边缘化，而男性的观点与声音则占据着优先报道的空间。即便女性能够出现在新闻报道中，也往往以“视觉点缀”的形象呈现，这进一步强化了女性的价值在于提供视觉愉悦，而非其观点本身的内涵。迄今为止，支撑此类论断的实证证据往往基于小规模的人工新闻内容分析。本文呈现了我们针对英语在线新闻媒体中的性别表征（gender representation）开展的大规模数据驱动研究的核心发现。为全面展现在线新闻中性别表征的全貌，我们同时对新闻文本与图像进行了分析。本研究分析的新闻语料库（corpus）涵盖了6个月内从950余家不同新闻机构采集的2353652篇新闻报道。我们从该初始数据集中提取出2171239条具名人物的提及记录，以及1376824张图像，并通过自动化计算方法（automated computational methods）识别出姓名与面部对应的性别属性。研究结果显示，在图像与文本两类载体中，男性的出现频率均高于女性，但该比例会因报道话题、新闻机构及报道形式的不同而产生变化。此外，几乎在所有话题与新闻机构的报道中，图像内女性的占比均持续高于文本；相较于作为新闻角色或信源被提及，女性更常以视觉形象出现在新闻报道中。本项大规模数据驱动分析，为新闻内容中男女两性被表征方式的宏观模式提供了关键的实证依据。