G2TT
来源类型Discussion paper
规范类型论文
来源IDDP16624
DP16624 Visual Representation and Stereotypes in News Media
Elliott Ash; Ruben Durante; Mariia Grebenshchikova; Carlo Schwarz
发表日期2022-05-01
出版年2022
语种英语
摘要We propose and validate a new method to measure gender and ethnic stereotypes in news reports, using computer vision tools to assess the gender, race and ethnicity of individuals depicted in article images. Applying this approach to 700,000 web articles published in the New York Times and Fox News between 2000 and 2020, we find that males and whites are overrepresented relative to their population share, while women and Hispanics are underrepresented. Relating images to text, we find that news content perpetuates common stereotypes such as associating Blacks and Hispanics with low-skill jobs, crime, and poverty, and Asians with high-skill jobs and science. Analyzing news coverage of specific jobs, we show that racial stereotypes hold even after controlling for the actual share of a group in a given occupation. Finally, we document that group representation in the news is influenced by the gender and ethnic identity of authors and editors.
主题Political Economy ; Public Economics
关键词Stereotypes Gender Race Media Computer vision Text analysis
URLhttps://cepr.org/publications/dp16624-3
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/546284
推荐引用方式
GB/T 7714
Elliott Ash,Ruben Durante,Mariia Grebenshchikova,et al. DP16624 Visual Representation and Stereotypes in News Media. 2022.
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