Assessing Attractiveness in Online Dating Profiles Andrew T. Fiore

Lindsay Shaw Taylor

G.A. Mendelsohn

Marti Hearst

School of Information

Department of Psychology

University of California, Berkeley

In the U.S.:

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

64% of online dating users think the large pool helps people find a better date 47% of all online adults concur

Source: Pew Internet and American Life Project

Perception and attraction,offline and online

Performing & perceiving self

Performance of identity “giving” vs. “giving off” (Goffman 1959)

Great capacity for control in online performance

Signals convey information with varying degrees of certainty (Donath 1999) Conventional vs. assessment

Social Information Processing, hyperpersonal comm. (Walther 1992, 1996)

What’s in a profile?

Combination of fixed-choice 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 mislead a little (Hancock et al. 2007)

Do they reflect actual self? Ideal self?

PHilton81Age: 27Height: 5’8”Weight: 115 lbsOccupation: Heiress

ABOUT ME“People say they envy my lifestyle, but I'm convinced that anyone with a little imagination can live ‘The Life.’”

Sources: Wikipedia, “Confessions of an Heiress,” Reuters

Perceptions of profiles

Substantial inferences from small cues — Walther’s SIP (Ellison et al. 2006)

“Thin slices,” big inferences from bits of Facebook profiles (Stecher & Counts 2008)

Fiore & Donath (2005) Messages received as proxy for attractiveness Different predictors for men and women

Norton, Frost, & Ariely (2007) More information, less liking (better discrimination)

Norton, Frost, & Ariely (2007)

Methodology

Profiles (rating targets)

50 Yahoo! Personals profiles with photos

25 men, 25 women, 20 to 30 years old

5 of each from Atlanta, Boston, San Diego, Seattle, and St. Louis (geographic diversity)

One profile randomly chosen from each of the first five pages of search results

Random sample of recently active users

Fixed choice

Fixed choice

Fixed choice

Free text

Photo

Rating dimensions for profiles

Attractive

Genuine, trustworthy

Masculine

Feminine

Warm, kind

Self-esteem

Extraverted

Self-centered

Procedure

Participants provide information about age, gender, sexual preference. We provided only profiles and pieces of the

appropriate target gender.

Rate randomly ordered profiles and pieces through the Web application for 50 min.

Indicate own self-esteem and attractiveness on Likert-type scale.

Debriefing, payment.

Participants (raters)

Recruited through UC-Berkeley Xlab

41 women, 23 men, heterosexual

66% Asian

Between 19 and 25 years old (median 21)

Self-reported attractiveness: mean 2.8 on 0 to 4 scale

Self-reported self-esteem: mean 2.7 on 0 to 4 scale

Results

Raw data and standardization

Each profile and profile component rated by multiple participants: 29,120 total ratings

“Ipsatization”: standardize by each participant, for each dimension Scales are arbitrary — what is “high” or “low”

for a given participant, for a given dimension?

Averaged ratings of each profile and profile piece on each dimension Necessary because data are sparse — few

participants rated every piece of every profile

Checking for repetition effects

Participants rated more than one piece from each profile — is this a problem?

They never rated the exact same piece twice.

Whole profiles generally presented after pieces.

No systematic differences in ratings upon first exposure to a piece of a given profile and subsequent ratings of other pieces.

Bottom line: We can safely use all the ratings for our analysis.

Attractivenessof whole profiles

Dimensions of whole profiles

Whole profiles and pieces

Men’swhole-profileattractiveness

Women’swhole-profileattractiveness

Photo attractiveness .88 .87

Free-text attractiveness .71 .27

Fixed-choice attractiveness .47 .23

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Whole profiles and pieces

Attractivenessof profile pieces

Attractiveness of photos

Attractiveness of free text

Putting it together

The big picture: Modeling whole-profile attractiveness

Men’s profiles+ Photo attractive

+ Free-text attractive

+ Masculine

– Warm and kind in photo

+ Genuine/trustworthy in photo

+ Photo attractive x fixed-choice attractive x free-text attractive

Women’s profiles+ Photo attractive

+ Free-text attractive

– Masculine

+ Extraverted

+ Self-esteem in photo

+ Feminine in photo

Men’s whole profile attractiveness

What wasn’t associatedwith attractiveness Attractiveness of fixed-choice components

(after adjusting for other component effects)

Self-rated self-esteem or attractiveness of participants

Length of text in free-text piece

Use of positive or negative emotion words or self-references in profile text (measured with LIWCS)

Limitations Purely associational data, not causal

Representativeness of participant sample Asians overrepresented among raters — 

problematic for studying attractiveness

What is good is beautiful; what is beautiful is good (Dion et al. 1972) — a halo effect? But not all desirable dimensions were associated

with attractiveness

What do averages mean for dyadic phenomena?

What’s next?

Systematically combine attractive and unattractive components — what dominates?

Examine deal-makers and deal-breakers

What role do the categorical pieces play in the process of identifying potential dates?

Identify pairs of users about to meet —how do their perceptions based on profiles change when the meet face to face?

Thank you! Any questions?

Andrew T. Fiore

Lindsay Shaw Taylor

G.A. Mendelsohn

Marti Hearst

For more information:

http://www.ischool.berkeley.edu/~atf/

atf@ischool.berkeley.edu

Thanks to the National Science Foundation and Microsoft Research for sponsoring this work.