DP16624 Visual Stereotypes in News Media

Author(s): Elliott Ash, Ruben Durante, Mariia Grebenshchikova, Carlo Schwarz
Publication Date: October 2021
Date Revised: October 2021
Keyword(s): Computer Vision, gender, media, race, Stereotypes, Text Analysis
JEL(s): C45, J15, J16, L82, Z1
Programme Areas: Public Economics, Political Economy
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=16624

We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text and, crucially, the interaction between the two. We apply this approach to over 2 million web articles published in the New York Times and Fox News between 2000 and 2020. We find that in both outlets, men and whites are generally over-represented relative to their population share, while women and Hispanics are under-represented. News content perpetuates common stereotypes such as associating women with narratives about caring roles and family; Blacks and Hispanics with low-skill jobs, crime, and poverty; and Asians with high-skill jobs and science. There are some significant differences across outlets, with Fox's content displaying a stronger association of Hispanics with immigration than the New York Times. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors. This suggests that it results, at least in part, from the choices of news makers, and could change in response to increased diversity in newsroom staff.