Coye Cheshire & Andrew Fiore 4 April 2012 // Computer-Mediated Communication Intimate Relationships.

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Coye Cheshire & Andrew Fiore 4 April 2012//

Computer-Mediated Communication

Intimate Relationships

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Romantic love —  a timeless tradition?

Mediated meeting

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http://blog.modernmechanix.com/2008/04/08/boy-girl-computer/

4/4/2012

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Thousands of boys and girls who’ve never met plan weekends together, for now that punch-card 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/

online

4/4/2012

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Pew online dating survey (2006)

63m know someone who has used a dating site16m have used a dating site themselves

53m know someone who has gone on a date7m 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 date47% of all online adults concur

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designers

designers

designers

Social shaping of technology

Online dating: The basics

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10

Fixed choice

Fixed choice

Fixed choice

Free text

Photo

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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?

Searching

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Matching

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Conceptual lenses

CMCMate selection

Searching/MatchingSocial networks Marriage markets

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?

Individuals

Dyads

Populations

Mate selection: Two perspectivesEvolutionary psychology

Claims we seek and offer traits associated with reproductive success, so:

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.

Assortative mating

Claims we partner with people like us (homophily).

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.

19Computer-Mediated Communication — Cheshire & Fiore4/4/2012

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7 10

4

85

2 8 67

3

95

6

8

53 2

6

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7 10

48

52

8 6

7

3

956

8

532

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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4/4/2012 Computer-Mediated Communication — Cheshire & Fiore 26(Gupta & Singh 1982)

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The process of online dating

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Pieces of profiles:What predicts attractiveness?

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Photo × Text attractiveness

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Photo high

Photo med

Photo low

Women’s profiles Men’s profiles

Tex

t lo

w

Tex

t m

ed

Tex

t hi

gh

Tex

t lo

w

Tex

t m

ed

Tex

t hi

gh

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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 whatthey 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 perfectfrom 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.

I really like Billy Joel.?

Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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4/4/2012 Computer-Mediated Communication — Cheshire & Fiore 52

Norton, Frost, & Ariely (2007)

Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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“Dissimilarity cascades”

Norton, Frost, and Ariely (2007)

1. People think more knowledge = more liking

2. Actually, more traits = less liking

3. Similarity mediates the relationship in (2)

4. Dissimilarity cascades

5. Moving from the lab to real dates: Knowledge, liking, similarity before and after

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Norton, Frost, & Ariely (2007)

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 face-to-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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p < .001

p < .01***

**

***

***

***

***

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(Fio

re e

t al.)

60

on

line

da

ting

ma

ga

zin

e.c

om

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p < .01

p < .001

p < .001

p < .001

(Fio

re e

t al.)

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Who seeks, contacts, and replies to whom?

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Age

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Age: Sought, contacted, replied to

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n > 1,000,0004/4/2012

Race

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Race: Preference analysis

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n > 1,000,000

Proportion of users who sought and contacted only people of the same race by age and sex

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Race: Contact analysis

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n > 1,000,000

Average proportion of contacts to same race by age and sex

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Religion

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Religion: Preference analysis

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n > 1,000,0004/4/2012

Religion: Contact analysis

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n > 1,000,0004/4/2012

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 woman19.2 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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