The Social Stratification of Fame Arnout van de

The Social Stratification of Fame Arnout van de

The Social Stratification of Fame Arnout van de Rijt Stony Brook University Steven Skiena Stony Brook University Charles Ward Stony Brook University Eran Shor McGill University Missing in Stratification Research income wealth education occupational prestige status health

x fame Introduction Measuring Fame Data Sources Analysis Strategy Results Definition of Fame Emergent Sociology of fame and celebrity (Ferris 2007): pure renown literally the sum of all people who have heard of a persons 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 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 Celebrity Status Well-known for being well-known (Boorstin 1961) Paris Hilton Kim Kardashian Musical Chicago Short public attention span to celebrities Introduction Measuring Fame Data Sources

Analysis Strategy Results Reality TV Ordinary people swiftly rise to fame, only to be replaced in the next season Introduction Measuring Fame Data Sources Analysis Strategy Results In / Out Lists Whos news? Whos not?

http://www.washingtonpost.com Introduction Measuring Fame Data Sources Analysis Strategy Results 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 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 longhas 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 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 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 Fame as a Stratification Variable income education

wealth status political power health fame Introduction Measuring Fame Data Sources Analysis Strategy Results 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 Research Question

Fame: Fleeting or Stratified? Introduction Measuring Fame Data Sources Analysis Strategy Results Overview Measuring Fame Data Sources Analysis Strategy Results Introduction Measuring Fame

Data Sources Analysis Strategy Results 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 Duration of a News Item 1. Production: 2/3 of a news thread occurs in 24 hours (Leskovec et al. 2009:503) 2. Consumption: It takes 35 hours for 1/2 of an articles readers to click on the article (Barabsi 2010:4950) Introduction Measuring Fame

Data Sources Analysis Strategy Results 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 person name as unit of analysis. Introduction Measuring Fame Data Sources Analysis Strategy

Results 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 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 1977Introduction Measuring Fame Data Sources Analysis Strategy Results 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 Analysis Challenge: Common Names 1 2 3 Introduction

Measuring Fame Data Sources Analysis Strategy Results 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 NYT Fame Across Two Decades Introduction Measuring Fame Data Sources Analysis Strategy Results Deck Stacked in Favor of High 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 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 Mobility Table Fame in current year 0 1-10 11-100 101-1000

1001+ total 1-10 67 28 5 0 0 100% 11-100 14

29 53 5 0 100% 101-1000 0 3 24 68

4 100% 1001+ 0 0 0 30 70 100% Fame in past year # sentences

Introduction Measuring Fame Data Sources Analysis Strategy Results Mobility Table Fame in current year 0 1-10 11-100 101-1000

1001+ total 1-10 67 28 5 0 0 100% 11-100 14

29 53 5 0 100% 101-1000 0 3 24 68

4 100% 1001+ 0 0 0 30 70 100% Fame in past year # sentences

Introduction Measuring Fame Data Sources Analysis Strategy Results Mobility Table Fame in current year 0 1-10 11-100 101-1000

1001+ total 1-10 67 28 5 0 0 100% 11-100 14

29 53 5 0 100% 101-1000 0 3 24 68

4 100% 1001+ 0 0 0 30 70 100% Fame in past year # sentences

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

Results Mobility in Fame Sudden loss of fame Introduction Measuring Fame Data Sources Analysis Strategy Results 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 4 Ways to Identify Celebrities 1. Only names that appear in tabloids 2. Names that appear for 50+% in entertainment sections of newspapers 3. For all names, only count appearances in entertainment sections 4. Only movie actors (IMDb)

Introduction Measuring Fame Data Sources Analysis Strategy Results Tabloids in Database 6 scandal-, crime-, gossip-, fashion- or celebrityoriented journals: Sun (UK) USA Weekend

Hollywood Reporter New York Post New York Daily News Womens Wear Daily Introduction Measuring Fame Data Sources Analysis Strategy Results 4 Ways to Identify Celebrities 1. Only names that appear in tabloids 2. Names that appear for 50+% in entertainment sections of newspapers 3. For all names, only count appearances in entertainment sections

4. Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results 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 4 Ways to Identify Celebrities 1. Only names that appear in tabloids 2. Names that appear for 50+% in entertainment sections of newspapers 3. For all names, only count appearances in

entertainment sections 4. Only movie actors (IMDb) Introduction Measuring Fame Data Sources Analysis Strategy Results 4 Ways to Identify Celebrities 1. Only names that appear in tabloids 2. Names that appear for 50+% in entertainment sections of newspapers 3. For all names, only count appearances in entertainment sections 4. Only movie actors (IMDb) Introduction

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

Measuring Fame Data Sources Analysis Strategy Results Correlations: Blogs vs. Newspapers Introduction Measuring Fame Data Sources Analysis Strategy Results 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 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 Stability of Fame at Different Scales Introduction Measuring Fame Data Sources

Analysis Strategy Results 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 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 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

Life Course of a Name Introduction Measuring Fame Data Sources Analysis Strategy Results Evaluation of Hypothesis Hypothesis of high mobility is rejected Mobility in stratification of fame appears low Mobility low even among celebrities Mobility low on both blogs and newspapers Mobility low for shorter time windows Fame, even at low levels ( dozen articles p.y.), is steady Introduction

Measuring Fame Data Sources Analysis Strategy Results Conclusion Scholarly consensus on high mobility in fame finds no support in news analysis Fame appears no less stable in social media than in newspapers and no less so among entertainers than among politicians Stratification of fame is more rigid than generally believed; celebrity culture and celetoids appear of limited impact Just like other stratification variables, fame remains relatively stable throughout the life course. Thank You

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