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Across Scandinavia, one can witness a situation where gender equality has previously been at the forefront of the political and societal agenda, but where progress now seems to be slowing down. The news media is a domain where this negative development is particularly pronounced, and several studies have established that the Scandinavian news media...
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Context 1
... the expert is especially important, and once again our findings support prior studies (Niemi & Pitkänen, 2017). Figure 3 depicts the overall numerical distribution of gender by subject and source type, and again, men dominate all subject and source types both in total numbers and in percentages. to the readers' perspective, our study shows that for each female expert that readers encounter, they will have met more than three male experts, in concordance with GMMP (2020), where only 25 per cent of the Danish experts were women. ...
Citations
... Indeed, the majority of previous work on racial biases in the news has examined the portrayal of minority racial groups in television news coverage [13][14][15]. With regards to gender bias, a handful of studies have examined gender representation in news images [30,35], finding that gender bias differs by topic and type of news venue. Yet, these studies largely examine a narrow temporal scope, focusing on articles or images published over the course of a year at most. ...
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.
