Representation of Women and Minorities in Media
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This week we discuss about content, and the methods to quantify them.
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Representation of Women and Minorities in Media
- 1. Intro to the Media Spring 2017 – MACS 101
- 3. Content • 1. The *subject matter of a *text or *message, as distinct from its *form or *style. Informational texts tend to foreground content (in contrast to aesthetic texts). Content can only be separated from form for analytical purposes. The idea that content (or thought) precedes form has often been criticized; from a *structuralist perspective there is no content before form. 2. What is denoted, depicted, or otherwise represented. 3. (informational content) The *information in a message. In *information theory this is based on the predictability of letters or digits in a sequence.
- 4. • Content is the part of media with which we are most familiar, and which is nearly impossible to avoid. • it is important to understand that the words 'content' and 'representations' refer to different methodologies for studying the same thing
- 5. Producer Message Consumer Contents Political Economy
- 7. • Content will refer to the quantitatively derived measurement and analysis of media messages. • Representation will refer to the qualitatively derived analysis of media messages and will be discussed at length next week.
- 8. What to study? • We study content to determine whether there are patterns, tendencies, foregroundings, or omissions in the media's depiction of topics, groups, and individuals
- 9. Questions studied through content analysis • Do anti-pregnancy public service announcements overrepresent female teens? • What type of content gets taken of Facebook? • Do young teens posts in Snap Chat differ from those of young adults? • What type of sexting classifies as bullying? • What types of human targets do digital first person shooter games contain? • Is a particular political party foregrounded in a news channel? • Is Donald Trump given more coverage than other Republican Party presidential hopefuls? • How is global warming covered in different news channels? • Are women less often used as news sources and more often used to sell domestic products in advertisements? • Do single women sell different products than mothers in advertising? • What types of roles do women play in Hollywood film? • Are Julian Assange and Mark Zuckerberg covered differently by the press; one of them as outlaw and the other one as heroic innovator?
- 10. Four steps • Finding your images • devising your categories for coding • coding the images • analysing the results
- 11. • Find a keyword to search on google images
- 12. Optional • Create a quilt so you will have a randomized set
- 14. Coding • `Coding' means attaching a set of descriptive labels (or `categories') to the images.
- 15. • In their study of National Geographic , Lutz and Collins chose one photo at random from each of the 594 articles on non-Western people published between 1950 and 1986
- 16. Codes • world location • unit of article organization (region, nation-state, ethnic group, other) • number of photographs including Westerners in an article • smiling in a photograph • gender of adults depicted • age of those depicted • aggressive activity or military personnel or weapons shown • activity level of main foreground figures • activity type of main foreground figures • camera gaze of main person photographed • surroundings of people photographed ritual focus • group size • Westerners in photograph • Urban versus rural setting • wealth indicators in photograph • skin colour • dress style (`Western' or local) • male nudity • female nudity • technological type present (simple handmade tools, machinery) • vantage (point from which camera perceives main figures)
- 17. • As Lutz and Collins (1993: 89) say, the process of reducing the rich material in any photograph to a series of codes is just that: a reduction in which much will be lost. The key point to remember, though, is that the images must be reduced to a number of component parts which can be labelled in a way that has some analytical significance.
- 18. Working groups • Live Events • Mobilities • New uses of the Old • Personal Media • Politics of representation
- 19. Live Events
- 20. • Diana - Victoria’s Secret Show • Noa – Techno Music • Ethan – College Football • Chuqiao – New Year’s Spring Gala • Zepei – Public movies
- 22. • Matthew • Yuren • Rayan • Raja • Diya • From devices to uses of media in the city.
- 23. New uses of the old
- 24. • Brian, Ishani, Mehnoor – TV • Michael – Telegraph • Zac – Radio • Cindy - Recorder
- 25. Personal Media
- 26. • Bin – WeChat • Paul – Twitch.tv • Emily J. – Instagram • Emily D. - Facebook • Jason – TV (streaming) • Abdallah - Iphone camera
- 27. Representation and socio- political change
- 28. • Emily A. – Feminist movies • Karina – Law and Order (crime shows) • David – South Park • Ivy – Meteor Garden • Blake – War movies
- 29. For next class • Bring your screenshot or quilt to class
- 30. • Women (their face and hair) • Celebrities • Fashion Items • Cold tone of color Patterns
- 31. • Women’s half-naked body • Male Athletes’ names and full-length portraits • Blue/Green tone of Color Patterns
- 32. Patterns?
- 33. Why do Content Analysis? • Content Analysis enables a more objective evaluation than comparing content based on the impressions of a listener. • Using Quantitative Method = the results of content analysis are numbers and percentages. - to remove much of the subjectivity from summaries - to simplify the detection of trends. • Useful to clarify Cause-Effect (causal) relationship of the case
- 34. Is Hollywood Racist? Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014)
- 35. • Analyzing 3,932 speaking characters from 100 of the top- grossing film of 2013 in U.S.. • A full 74.1% were White, 14.1% Black, 4.9% Hispanic, 4.4% Asian, 1.1% Middle Eastern, <1% American Indian or Alaskan Native, and 1.2% were from "other" races/ethnicities. • No meaningful change has been observed in the frequency of any racial/ethnic group on screen in 600 popular films between 2007 and 2013. Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014)
- 36. U.S. Census 2014 62.1% 17.4% 13.2% 1.2% 6.1% underrepresented Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014)
- 37. underrepresented Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014)
- 38. underrepresented Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014)
- 39. Race/Ethnicity in 600 Popular Films: Examining On Screen Portrayals and Behind the Camera Diversity (2014) trivialized, victimized, or sensationalized.
- 40. “Every Single Word Spoken” [http://everysinglewordspoken.tumblr.com]
- 41. Case • Imagine you are working on a study on Nickelodeon commercials in 2009. • https://www.youtube.com/watch?v=miEF53yhMI0
- 42. • In your group, think of what codes you are going to count in the commercials • Select six of them. • Each member must count the frequencies – of one code (how many times does the topic show up)
- 43. • Present the result as percentages
- 44. Intercoder reliability • Now each one of you present your topic to the group • Describe what patterns have you seen in your images • Add 2-3 minutes to hear feedback from the group
- 45. Symbolic Annihilation • Women and minorities were • 1) underrepresented in the media, or • 2) when women and minorities were represented they tended to be trivialized, victimized, or sensationalized.
Editor's Notes
- by Stacy Smith et al.,
- by Stacy Smith et al.,
- Hispanic females (37.5%) were more likely than females from all other races/ethnicities to be shown partially or fully naked on screen. In comparison to black females (23.5%), White females were more likely to be shown with some exposed skin (31.9%) and Asian females were less likely (18.2%). Hispanic males (16.5%) were the most likely to be shown in tight, alluring, or revealing clothing. Asian males (13.7%) were more likely than white males (8.3%) to be depicted in sexy attire. In terms of some nudity, male characters from "other" (18.2%) races/ethnicities were more likely than white male characters (9.9%) to be shown partially or fully nude.
- Nearly a fifth of all films in the sample (17%) depict no African-American or black speaking characters across their unfolding narratives. Fifteen films depict black characters as 2.2-5.9% of the cast and another 22 movies portray black characters in 6-10% of the cast. Taken together, over half of the movies in the sample are under indexing in comparison to U.S. population statistics. Only 14% of the movies show black characters at or within 2 percentage points of U.S. Census (10.8-14.5%).
- Media creates meaning about race, ethnicity and gender, and plays an important role in shaping the way we understand race, ethnicity, and gender as part of our identity, our history, our social institutions, and our everyday lives. But seeing the media, how the women and minorities are portrayed?