Transcript slides - Courses

Computer-Mediated
Communication
Intimate Relationships
Coye Cheshire & Andrew Fiore
//
4 April 2012
Romantic love —
a timeless tradition?
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Mediated meeting
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http://blog.modernmechanix.com/2008/04/08/boy-girl-computer/
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Thousands of boys and girls
who’ve never met plan weekends
together, for now that punch-card
online
dating’s here, can flings be far
behind? And oh, it’s so right,
baby. The Great God Computer
has sent the word. Fate. Destiny.
Go-go-go.
— Look Magazine, February 1966
http://blog.modernmechanix.com/2008/04/08/boy-girl-computer/
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Pew online dating survey (2006)
63m know someone who has used a dating site
16m have used a dating site themselves
53m know someone who has gone on a date
7m have gone on a date themselves
29% of online adults think online daters desperate
(but only 20% of those single and looking)
64% of online dating users think the large pool
helps people find a better date
47% of all online adults concur
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Social shaping of technology
designers
designers
designers
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Online dating: The basics
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Photo
Fixed choice
Fixed choice
Free text
Fixed choice
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Online dating profiles
 Combination of categorical descriptors,
free text self-description, and photos
 Highly optimized self-presentations
 Carefully selected detail
 Unlimited time to craft
 Exaggerations? Lies?
 A lot of people lie a little (Hancock et al. 2007)
 Do they reflect actual self? Ideal self?
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Searching
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Matching
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Conceptual lenses
CMC
Mate selection
Searching/Matching
Social networks
Marriage markets
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Individuals
Dyads
Populations
?
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Mate selection: Two perspectives
Assortative mating
Evolutionary psychology
 Claims we partner with
people like us (homophily).
 Claims we seek and offer
traits associated with
reproductive success, so:
 Evident with regard to:
Physical attractiveness,
socioeconomic status,
race, adult attachment
style, personality traits,
among others.
 Yet sometimes it’s more
complicated than just
similarity.
Computer-Mediated
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Communication — Cheshire &18
Fiore
 Women seek men with
resources, signaled by
age, wealth, education,
height, etc.
 Men seek women with
fertility, signaled by youth,
facial symmetry, muscle
tone, etc.
7 10
9
2
3
57 5
6
5
8 6
8 6
2
4 8
3
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76
5
2
3
2
56
88
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10 9
75
34 8 6
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7 seeks 10 for an awkward time
“Marriage markets” — differential exchange
Some points to ponder:
 Why wouldn’t a 7 want a 10?
 What stops us from trading up repeatedly?
 Opportunity cost of staying with current mate?
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The tyranny of choice, or:
Gourmet jam is not a date
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(Gupta & Singh 1982)
The process
of online dating
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Pieces of profiles:
What predicts attractiveness?
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Photo × Text attractiveness
0.81
1.06
Photo High
1.49
0.52
0.44
0.72
Photo Med
Photo med
-0.6
0.19
Photo Med
0.02
-0.34
-0.81
-0.42
-1.83
-1.63
Photo Low
-1.54
-2.15
-2.11
-1.81
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Text Low
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Text Med
Text high
Text High
Text med
Text Med
Text low
Text Low
Text high
Text low
Photo Low
Photo low
Text med
-1
1
Photo High
Photo high
0
2
Women’s
profiles
Mean:
attractive profiles
(F profiles) Mean:Men’s
attractive
(M profiles)
Text High
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Strategic vs. authentic
vs. aspirational
self-presentation
Anticipated future interaction?
Actual self vs. ideal self?
“Balancing accuracy and desirability”
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Participants from Ellison et al.
 “In their profile they write about their
dreams as if they are reality.”
 “I’ve never known so many incredibly
athletic women in my life!”
 “I checked my profile and I had lied a little
bit about the pounds, so I thought I had
better start losing some weight so that it
would be more honest.”
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Forming impressions in online dating
 “Cognitive misers”: Making the most of
limited cues
 Social Information Processing
(Walther)
 Reciprocal re-use of what
they notice in others
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Most people are not startlingly beautiful or
magically attractive. But someone who
seems just moderately nice — to most
people — can flower under the imaginative
attention of a lover’s eye. Not … because
the lover is somehow gilding the other with
fictitious charms; but because the kind of
attention the lover brings allows less obvious
qualities to be seen and appreciated.
— Armstrong (2002)
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Deception?
(Hancock et al. 2007)
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Deception?
(Hancock et al. 2007)
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Deception?
(Hancock et al. 2007)
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Honestly…(?)
 And yet: in Gibbs et al. (2006), 94% said
they had not intentionally misrepresented
themselves.
 87%: Doing so is not acceptable.
 Still, they feel others are misrepresenting.
 Why? Ellison et al. (2006) —
Foggy mirrors, avoiding natural boundaries,
portraying ideal selves…
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Is it deception? Or is it…





Misperception of self (foggy mirror)
Different readings of ambiguous labels
Self-enhancement (no intent to deceive)
Ideal self rather than actual self
Circumvention of technological constraints
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The peril (and promise)
of ambiguity
(“everything looks perfect
from far away…”)
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Virtue in vagueness: Norah Jones
The persona in her songs — let’s not call it
Ms. Jones herself, because her life couldn't
be this dull — might have lived practically
anywhere in the developed world, at any
time during the last century. Somehow Ms.
Jones’s work has managed to make a
virtue of vagueness.
— The New York Times, Feb. 8, 2004,
via Norton, Frost, & Ariely (2007)
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I really like
good music.
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I really like
Billy Joel.
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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Norton, Frost, & Ariely (2007)
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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“Dissimilarity cascades”
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Norton, Frost, and Ariely (2007)
1.
2.
3.
4.
5.
People think more knowledge = more liking
Actually, more traits = less liking
Similarity mediates the relationship in (2)
Dissimilarity cascades
Moving from the lab to real dates:
Knowledge, liking, similarity before and after
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Norton, Frost, & Ariely (2007)
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Fiore et al.
Hypotheses: Pre-date/post-date
H1: Participants will rate their dates less attractive on
average after meeting face-to-face for the first time
than before.
H2: Levels of perceived commonality will be lower on
average after face-to-face meeting than before.
H3: Average ratings of how close a participant’s date is
to his/her ideal for a partner will be lower after faceto-face meeting than before.
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Key questions and scales





How well have you gotten to know [name]?
How much do you have in common with [name]?
How close is [name] to your ideal for a partner?
Overall, how attractive do you find [name]?
How much is [name] someone you could see
yourself: being friends with, dating casually,
dating seriously, possibly something more?
 Likert-type scale: 0 (not at all) – 6 (very much)
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5
Perceptions before and after meeting face-to-face
(contemporaneous)
Before meeting
After meeting
4
***
***
***
*** p < .001
** p < .01
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0
1
(Fiore et al.)
2
3
***
Knowledge
In common
Close to ideal
Attraction
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onlinedatingmagazine.com
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— Cheshire & Fiore
Interest in relationship types (contemporaneous)
p < .001
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30
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
0
1
2
3
4
5
6
30
60
2
60
30
60
(Fiore et al.)
0
60
30
Interest in
“something
more”
Frequency
p < .001
1
0
Frequency
p < .001
Interest in
serious dating
0
0
Frequency
p < .01
Interest in
casual dating
Post-date
0
Interest in
friendship
Frequency
Pre-date
Who seeks, contacts,
and replies to whom?
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Age
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Age: Sought, contacted, replied to
n > 1,000,000
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Race
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Race: Preference analysis
Proportion
Proportionof
ofusers
userswho
whosought
soughtand
andcontacted
contactedonly
onlypeople
peopl e
thesame
sameethnicity
race by age
andand
sexsex
ofofthe
by age
1.0
Actually contacted same
Said they were seeking same
Proportion
0.8
M F
0.6
0.4
0.2
0.0
M
F
20s
M
F
30s
M
F
40s
M
F
50s
M
F
60s
M
F
70s
n > 1,000,000
Average
proportion of contacts65to same ethnicity by age and se
Computer
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Among
-Mediated
users who initiated contact with at least two different people
0.2
Race: Contact analysis
0.0
M
F
20s
M
F
30s
M
F
40s
M
F
50s
M
F
60s
M
F
70s
Averageproportion
proportionofofcontacts
contacts
same
race by
Average
toto
same
ethnicity
byage
ageand
andsex
sex
Among users who initiated contact with at least two different people
1.0
Expected prop. same if random pairs
Proportion
0.8
0.6
0.4
0.2
0.0
M F
20s
M F
30s
M F
40s
n > 1,000,000
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M F
50s
M F
60s
M F
70s
Religion
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Religion: Preference analysis
Proportion of users who sought and contacted only peopl e
of the same religion by age and sex
1.0
Proportion
0.8
Actually contacted same
Said they were seeking same
M F
0.6
0.4
0.2
0.0
M
F
20s
M
F
30s
M
F
40s
M
F
50s
M
F
60s
M
F
70s
n > 1,000,000
Average proportion of contacts
to same religion by age and sex
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Among
-Mediated
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users who initiated contact
with at least two different people
0.2
0.0
Religion: Contact analysis
M
F
20s
M
F
30s
M
F
40s
M
F
50s
M
F
60s
M
F
70s
Average proportion of contacts to same religion by age and sex
Among users who initiated contact with at least two different people
1.0
Expected prop. same if random pairs
Proportion
0.8
0.6
0.4
0.2
0.0
M F
20s
M F
30s
M F
40s
n > 1,000,000
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M F
50s
M F
60s
M F
70s
Who replies?
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How late is too late to reply?
 Median time to first reply:
16.1 hrs for a man contacted by a woman
19.2 hrs for a woman contacted by a man
 Chance of follow-up by initiator declines
~0.7% per day that recipient waits to reply.
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