The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum.

翻译：新闻报导对不同种族与性别群体的呈现方式对塑造公众舆论具有重要影响。因此，理解这些群体在新闻媒体中的呈现方式具有显著的社会价值，并已成为计算机科学与社会科学领域的重要研究方向。然而，现有文献仍缺乏对网络新闻文章中不同种族与性别群体出现频率及其讨论语境的纵向研究。为填补这一空白，我们提出两种机器学习分类器以检测给定对象的种族与年龄。随后，我们构建了一个包含123,337张图片和441,321篇网络新闻文章的数据集，涵盖《纽约时报》与福克斯新闻两大来源，并通过两种计算方法考察其呈现特征。首先，我们分析了新闻文章嵌入图片中种族与性别群体的出现频率与显著程度，发现少数种族与性别群体普遍存在代表性不足的现象，且即使出现时，其呈现显著度也低于主流群体。此外，我们发现相较于福克斯新闻，《纽约时报》显著更多地呈现少数种族群体的图像。其次，我们考察了文章文本中少数种族群体的出现频率与语境特征。分析揭示了特定种族群体报道范围的局限性，以及不同群体在冲突情境中被呈现为受害者和/或施害者的频率差异。综合而言，本研究通过提供两种开源的新型图像种族与年龄检测分类器，并揭示美国政治光谱两端新闻机构的种族与性别偏见，为相关学术领域作出了贡献。