1. 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

2. Source: Pew Internet and American Life Project
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
Online dating as recently as 10 years ago was stigmatized as a matchmaker for the awkward, but now people have adopted it in droves.
63 million — 31% of American adults
16 million — 11% of American Internet-using adults
53 million — 26% of American adults
7 million — 5% of American Internet-using adults
So much research on how attraction works offline, but now we have people finding dates and spouses and parents of their children through computer-mediated channels, and we have comparatively little idea how they perceive attractiveness in this new medium.

3. Perception and attraction, offline and online

4. Wealth of research into how attraction works offline
Wealth of research into how attraction works offline. Facial symmetry, pheromonal compatibility (produce healthy children), waist-to-hip ratio, repeated exposure, personality similarity and complementarity (Klohnen and Mendelsohn 1998), shared interests, shared political views, exchange theory (ev psych -- men exchanging power and resources for women’s fertility).
Some of these findings remain relevant for online attractiveness. But when it comes to online dating, our initial assessments of attraction come from a very different source of information than we get from a glance or a conversation – they come from an online dating profile.

5. Sample profile from Yahoo Personals with the identifying information obscured for the purposes of this presentation.
Free text, categorical descriptors, a photo, a headline. This is the extent of the self-expression that's available in an online dating profile – this is how online dating users convey their identity, and how other users initially form impressions of the profile author. How does computer-mediated interpersonal perception work in this context? There are several theoretical perspectives that are relevant here.

6. 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)
SIP — inferences from whatever details are available
Hyperpersonal — heightened affinity when communicating through lean channels

7. 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?
Exaggerations/lies: —Dan Ariely: lie enough to get to coffee, but not so much that you don’t get to sex
—Direct self-description is hard, and prone to error.
Actual/ideal:
— Another reason people might be seen to be exaggerating or lying
—Profiles might be more aspirational than factual (ideal self). Ellison et al. (2006) provide some evidence for this (e.g., hiking, the foggy mirror)

8. Sources: Wikipedia, “Confessions of an Heiress,” Reuters
PHilton81
Age: 27
Height: 5’8”
Weight: 115 lbs
Occupation: Heiress
ABOUT ME
“People say they envy my lifestyle, but I'm convinced that anyone with a little imagination can live ‘The Life.’”
As an example, here’s a computer simulation of an actual online dating profile.
Fixed-choice categorical components, Free-text description, and a photo.
So how do people perceive these profiles? How do they decide who is attractive to them? Several studies have addressed this – here are a few that are more relevant to our work.
Sources: Wikipedia, “Confessions of an Heiress,” Reuters

9. 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)
Ellison et al.: The woman who thought sitting down in photo meant overweight – and she also made sure she was standing in hers.
Stecher (Steck-er) & Counts: Not online dating profiles per se, but fairly accurate judgments of how much they liked a person based on just 5 pieces of information from a much larger set.
Norton et al.: People think they’ll find someone more attractive the more info they have. In fact, when presented with more specific pieces of info, they like them less on average. Perhaps more information allows better discrimination – discern the difference between someone you would like to be with and someone you would not.
Fiore & Donath: # Sent messages first. For men, more messages if older, more educated, more self-rated physically attractive. For women, more messages if lighter build, more self-reported physically attractive, and have a photo.
Norton Frost Ariely: Better able to know WHETHER you will like someone with more information – seems to lead to lower liking on average.

10. Norton, Frost, & Ariely (2007)
Study 5, average ratings of person met via online dating before dates and after dates. After first date, more knowledge, less liking, and less perceived similarity (similarity mediates relationship between knowledge and liking).

11. Methodology

12. 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

13. Photo Fixed choice Free text
We broke these profiles into three pieces: photo only, fixed-choice (stitched together), and free text. We asked our participants to rate these pieces and the whole profiles on eight dimensions.

14. Rating dimensions for profiles
Attractive Genuine, trustworthy Masculine Feminine Warm, kind Self-esteem Extraverted Self-centered
Attractive: our outcome of interest
G/T: Considering the common perception that people lie in online dating profiles
Masc/fem: easily enacted social roles w/ clear norms for appropriateness. You can also mention that Jerry has work w/ Melissa Williams looking at how people detect gender online.
Warm/kind: Good indicator of Agreeableness (Big 5: Openness, Conscientiousness, Extraversion, and Neuroticism), and also evolutionary psych research (Buss & colleagues) show it's the #1 feature men & women want In a mate.
Self-esteem: Klohnen & Mendelsohn showed associative mating for self-esteem, and we're interested in exploring its role in selection of dating partners
Extraverted: Another of the big 5; like agreeableness, associated w/likability
Self-centered:

15. Rating interface — a custom web application.
0-4 Likert-type scale
Randomization of order of dimensions every time
Confidence ratings

16. Randomization of order of dimensions

17. Randomization of order of dimensions

18. Randomization of order of dimensions

19. 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.

20. 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

21. Results

22. 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

23. 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.

24. Attractiveness of whole profiles

25. Dimensions of whole profiles

26. Whole profiles and pieces
Men’s whole-profile attractiveness
Women’s whole-profile attractiveness
Photo attractiveness
.88
.87
Free-text attractiveness
.71
.27
Fixed-choice attractiveness
.47
.23
***
***
***
Zero-order correlations *

27. Whole profiles and pieces
Standard errors for coefficient estimates in parentheses.

28. Attractiveness of profile pieces
Except for fixed-choice pieces.

29. Attractiveness of photos
Men’s and women’s photos

30. Attractiveness of free text

31. Putting it together
Series of models attempting to predict whole-profile attractiveness from other qualities of the whole profiles AND the attractiveness and other qualities of pieces

32. 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
Considered interactions of attractiveness of the three pieces. No two-way interactions significant, but for men, the 3-way one was.

33. Men’s whole profile attractiveness
Standardized attractiveness scores on Y axis. To be considered above the mean on attractiveness, a man’s profile needed to have two out of the three pieces above the mean in attractiveness, and one of them had to be the photo. If a man’s profile had an unattractive photo, having attractive free text and fixed-choice components didn’t make up for it.

34. What wasn’t associated with 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)
In the interest of full disclosure…

35. 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?
Good is attractive: Applicable to photo piece and whole profiles -- But not all socially desirable dimensions correlated with attractiveness.
What do averages mean? Attractiveness is idiosyncratic and personal; attraction is a dyadic phenomenon, built on a pattern of reciprocal interaction. So taking averages as we did here doesn’t necessarily help us predict whether any given pair will find each other attractive.

36. 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?
Stage model — filter, then evaluate.

37. Thank you! Any questions?
Andrew T. Fiore
Lindsay Shaw Taylor
G.A. Mendelsohn
Marti Hearst
For more information:
Thanks to the National Science Foundation and Microsoft Research for sponsoring this work.