The Social Stratification of Fame

1. The Social Stratification of Fame Arnout van de Rijt Stony Brook University Charles Ward Stony Brook University Steven Skiena Stony Brook University EranShor McGill University

2. Missing in Stratification Research • income • wealth • education • occupational prestige • status • health • fame Introduction Measuring Fame Data Sources Analysis Strategy Results

3. Definition of Fame Emergent “Sociology of fame and celebrity” (Ferris 2007): • “pure renown – literally the sum of all people who have heard of a person’s name.”(Currid-Halkett 2010:29,66) • continuous, must include also intermediates: "local newscasters, minor league athletes, or local politicians" (Ferris 2010:393) fame Introduction Measuring Fame Data Sources Analysis Strategy Results

4. Claim to Fame: High Mobility Sociology of fame & celebrity claims: Fame exhibits high mobility The hierarchy of fame exhibits continual change. Makes reference to popular notion of fleeting fame. Introduction Measuring Fame Data Sources Analysis Strategy Results

5. Celebrity Status “Well-known for being well-known” (Boorstin 1961) Paris Hilton Kim Kardashian

6. Musical Chicago Short public attention span to celebrities Introduction Measuring Fame Data Sources Analysis Strategy Results

7. Reality TV Ordinary people swiftly rise to fame, only to be replaced in the next season Introduction Measuring Fame Data Sources Analysis Strategy Results

8. In / Out Lists Who’s news? Who’s not? http://www.washingtonpost.com Introduction Measuring Fame Data Sources Analysis Strategy Results

9. Scholars on High Mobility (1/3) • "Fame bubbles can burst as quickly as they formed" (Cowen 2000:15) • “types who command media attention one day and are forgotten the next" (Rojek 2001:20-1) • “upward and downward mobility is a continuous characteristic” (Rojek 2001:21) • "ephemeral nature of fame" (Marshall 2004:3) • … Introduction Measuring Fame Data Sources Analysis Strategy Results

10. Scholars on High Mobility (2/3) • … • “it can be attached to and detached from individuals relatively easily." (Marshall 2004:3) • “celebrity does not usually last very long…has a flexible association with wealth” (Ferris 2007:373) • “increased mobility” of fame (Ferris 2007:375) • “time span between the rise and evaporation of celebrity is getting shorter.” (Currid 2010:219) • … Introduction Measuring Fame Data Sources Analysis Strategy Results

11. Scholars on High Mobility (3/3) • … • “status on speed. It confers honor in days, not generations; it decays over time, rather than accumulating; and it demands a constant supply of new recruits, rather than erecting barriers to entry.” (Kurzman et al. 2007:347) • “celebrity status is likely to be less stable than more traditional forms of status.” (Milner 2010:383) Introduction Measuring Fame Data Sources Analysis Strategy Results

12. Consensus without Evidence • Claim that fame exhibits high mobility • Scholarly consensus • Concordant with popular notion of fleeting fame • But: no systematic evidence has been acquired to confirm this claim

13. Fame as a Stratification Variable • income • education • wealth • status • political power • health • fame Introduction Measuring Fame Data Sources Analysis Strategy Results

14. Mechanisms for Stratification 1. Fame is associated with other resources that exhibit low mobility 2. Cumulative advantage in careers: Merton 1968 (science) Lang & Lang 1988 (visual arts) Allen & Parsons 2006 (sports) Salganik et al. 2007 (music) 3. Reinforcement in news making & habitual journalism: Molotch & Lester 1974 (habitual news making) Bielby & Bielby 1994 (recycling of past stars) Oliver & Myers 1999 (routine journalism) Vasterman 2005 (self-reinforcing themes) Introduction Measuring Fame Data Sources Analysis Strategy Results

15. Research Question Fame: Fleeting or Stratified? Introduction Measuring Fame Data Sources Analysis Strategy Results

16. Overview • Measuring Fame Data Sources Analysis Strategy Results Introduction Measuring Fame Data Sources Analysis Strategy Results

17. Measuring Fame as Media Coverage • Close correspondence between how widely known an individual is and how often he or she is referenced in the media Consistent with: • Agenda-setting theory(McCombs & Shaw 1972): Mass media determine what people judge to be important • Sociological definition of fame (Ferris 2007): Volume, not sentiment (infamy = fame); “any publicity is good publicity” Introduction Measuring Fame Data Sources Analysis Strategy Results

18. Duration of a News Item 2. Consumption: It takes 35 hours for 1/2 of an article’s readers to click on the article (Barabási 2010:49–50) 1. Production: 2/3 of a news threadoccurs in 24 hours (Leskovecet al. 2009:503) Introduction Measuring Fame Data Sources Analysis Strategy Results

19. Public Attention to a Person But: The persons involved in a news item may outlive the news item by reappearing in a later news item.  To study fame we must take the personname as unit of analysis. Introduction Measuring Fame Data Sources Analysis Strategy Results

20. Operationalization of Fame Number of appearances of a name in newspaper records 6 1 7 2 3 4 7x Marc Meyers 5 Introduction Measuring Fame Data Sources Analysis Strategy Results

21. Data Sources Lydia news analysis system: (details in: Bautin et al. 2010) • Dailies corpus: For 2,500 newspapers: all person names online 2004- • Archival corpus: For 13 newspapers: all person names in scans 1977- Introduction Measuring Fame Data Sources Analysis Strategy Results

22. Data Processing • Lydia extracts person names from text through NLP algorithms study ‘typical’ person name in the news (all mentions of a name) • Classification of sections: entertainment, business, sports • NLP: gender, ethnicity, sentiment, geography Introduction Measuring Fame Data Sources Analysis Strategy Results

23. Analysis Challenge: Common Names 1 2 3 Introduction Measuring Fame Data Sources Analysis Strategy Results

24. Analysis Challenge: Common Names Common names may refer to multiple people (e.g. ‘Michael Jackson’) Strategy: Determine commonality via U.S. Census data. Uncommon := expected frequency in U.S. population < 1, assuming independence of first & last names  71% uncommon Outcome: Analysis of subsample of uncommon names shows robustness of key findings Introduction Measuring Fame Data Sources Analysis Strategy Results

25. NYT Fame Across Two Decades Introduction Measuring Fame Data Sources Analysis Strategy Results

26. Deck Stacked in Favor ofHigh Mobility Hypothesis • Event-based news coverage (book release, tournament, movie premiere)  inflates mobility • Newspaper format changes  inflates mobility Introduction Measuring Fame Data Sources Analysis Strategy Results

27. Mobility Analysis Classic method: Mobility table Typical unit of time: Years (e.g. Aaberge et al. 2002) Cross-tabulate fame in one year by fame in the subsequent year Introduction Measuring Fame Data Sources Analysis Strategy Results

28. Mobility Table Introduction Measuring Fame Data Sources Analysis Strategy Results

29. Mobility Table Introduction Measuring Fame Data Sources Analysis Strategy Results

30. Mobility Table Introduction Measuring Fame Data Sources Analysis Strategy Results

31. Mobility in Fame Introduction Measuring Fame Data Sources Analysis Strategy Results

32. Mobility in Fame Sudden loss of fame Introduction Measuring Fame Data Sources Analysis Strategy Results

33. Results So Far • Results so far: • Mobility in stratification of fame appears low • How robust is this finding across different categories of individuals? • Stratification of ‘celebrities’ may exhibit greater mobility than that of institutional fame (e.g. politicians) Introduction Measuring Fame Data Sources Analysis Strategy Results

34. 4 Ways to Identify Celebrities • Only names that appear in ‘tabloids’ • Names that appear for 50+% in entertainment sections of newspapers • For all names, only count appearances in entertainment sections • Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results

35. ‘Tabloids’ in Database 6 scandal-, crime-, gossip-, fashion- or celebrity-oriented journals: • Sun (UK) • USA Weekend • Hollywood Reporter • New York Post • New York Daily News • Women‘s Wear Daily Introduction Measuring Fame Data Sources Analysis Strategy Results

36. 4 Ways to Identify Celebrities • Only names that appear in ‘tabloids’ • Names that appear for 50+% in entertainment sections of newspapers • For all names, only count appearances in entertainment sections • Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results

37. Top 10 ‘Entertainers’ in Sample Names with 50+% mentions in newspaper entertainment sections: • Jamie Foxx -- musician / actor / comedian talk radio host • Bill Murray -- actor / comedian • Natalie Portman -- actor • Tommy Lee Jones -- actor / film director • Naomi Watts -- actor • Howard Hughes -- film producer / director / entrepreneur / aviator / engineer • Phil Spector -- record producer / song writer • John Malkovich -- actor / producer / director / designer • Adrien Brody -- actor / film producer • Steve Buscemi -- actor / film director Introduction Measuring Fame Data Sources Analysis Strategy Results

38. 4 Ways to Identify Celebrities • Only names that appear in ‘tabloids’ • Names that appear for 50+% in entertainment sections of newspapers • For all names, only count appearances in entertainment sections • Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results

39. 4 Ways to Identify Celebrities • Only names that appear in ‘tabloids’ • Names that appear for 50+% in entertainment sections of newspapers • For all names, only count appearances in entertainment sections • Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results

40. Mobility among Celebrities Introduction Measuring Fame Data Sources Analysis Strategy Results

41. Results So Far • Results so far: • Mobility in stratification of fame appears low • Mobility low even among ‘celebrities’ • How robust is this finding across media? • Blogs may exhibit greater mobility than newspapers, given open democratic access Introduction Measuring Fame Data Sources Analysis Strategy Results

42. Mobility in Blogs vs. Newspapers Introduction Measuring Fame Data Sources Analysis Strategy Results

43. Correlations: Blogs vs. Newspapers Introduction Measuring Fame Data Sources Analysis Strategy Results

44. Results So Far • Results so far: • Mobility in stratification of fame appears low • Mobility low even among ‘celebrities’ • Mobility lower on blogs than in newspapers • We have measured fame as annual coverage • How stable is fame at different scales? Introduction Measuring Fame Data Sources Analysis Strategy Results

45. Trade-off in Window Size • We have measured fame as annual coverage • Trade-off in size of window: • Too narrow  even famous names fluctuate • Too wide  very brief fame is not detected • Explore narrower windows: quarters, months Introduction Measuring Fame Data Sources Analysis Strategy Results

46. Stability of Fame at Different Scales Introduction Measuring Fame Data Sources Analysis Strategy Results

47. Results So Far • Results so far: • Mobility in stratification of fame appears low • Mobility low even among ‘celebrities’ • Mobility lower on blogs than in newspapers • Mobility low also for shorter time windows • We have found that fame tends to persist from month to month, quarter to quarter, and year to year • How long does fame last? Introduction Measuring Fame Data Sources Analysis Strategy Results

48. Duration of Fame • Employ “Archival” database of select newspapers for which coverage goes back to 1977 (scans). • Study life course of a name. Define “new names” as those that never occurred during first 5 years of our data (1977-1981). Track coverage of new names during years since birth until we hit the present (right-censored). Introduction Measuring Fame Data Sources Analysis Strategy Results

49. Calculating Life Course of a Name • Select all new names from Archival corpus that did not occur in first 5 years (1977-1981) • Bucket names by annual coverage volume • For each year since birth calculate average coverage of all uncensored names in bucket • Normalize such that lifetime volume sums to 1 • Graph normalized coverage by age Introduction Measuring Fame Data Sources Analysis Strategy Results

50. Life Course of a Name Introduction Measuring Fame Data Sources Analysis Strategy Results