Visual Representation and Stereotypes in News Media

Arbeitspapier

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 as well as 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. We also document 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. For jobs, we show that the relationship between visual representation and racial stereotypes holds even after controlling for the actual share of a group in a given occupation. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 9686

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Entertainment; Media ; Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination ; Economics of Gender; Non-labor Discrimination ; Cultural Economics; Economic Sociology; Economic Anthropology: General ; Neural Networks and Related Topics
• Thema: stereotypes ; gender ; race ; media ; computer vision ; text analysis

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Ash, Elliott ; Durante, Ruben ; Grebenshchikova, Maria ; Schwarz, Carlo
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/260816
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ