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 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
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
Who’s news? Who’s 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 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
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
Lang & Lang 1988
Allen & Parsons 2006
Salganik et al. 2007
(science)
(visual arts)
(sports)
(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
article’s readers to click on the article (Barabási
2010:49–50)
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.

Introduction
To study fame we must take the person
name as unit of analysis.
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 past year
Fame in current year
Introduction
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%
# sentences
Measuring Fame
Data Sources
Analysis Strategy
Results
Mobility Table
Fame in past year
Fame in current year
Introduction
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%
# sentences
Measuring Fame
Data Sources
Analysis Strategy
Results
Mobility Table
Fame in past year
Fame in current year
Introduction
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%
# sentences
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
Women‘s 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
--
•
•
•
•
•
Bill Murray
Natalie Portman
Tommy Lee Jones
Naomi Watts
Howard Hughes
------
•
•
•
•
Phil Spector
John Malkovich
Adrien Brody
Steve Buscemi
-----
Introduction
Measuring Fame
musician / actor / comedian
talk radio host
actor / comedian
actor
actor / film director
actor
film producer / director /
entrepreneur / aviator / engineer
record producer / song writer
actor / producer / director / designer
actor / film producer
actor / film director
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